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This paper examines the attainable performance of various Doppler sublook or subband cross correlation techniques for vessel detection in synthetic aperture radar images. Research from the past two decades claims that these techniques were capable of improving the detection of small ships in challenging maritime environments. Despite many published experimental examples, a thorough analytical investigation corroborating this claim is noticeably absent. This paper is based on a rigorous theoretical analysis founded on the statistical properties of a textured sea surface model in thermal noise with simultaneous consideration of a constant false alarm rate. Emphasis has been placed on the correct accounting for detrimental physical effects caused by the Doppler spectrum being split into nonoverlapping parts, which have not been sufficiently considered in the literature to date. The theoretical results are confirmed via simulations and are substantiated with real RADARSAT-2 data. The analysis in this paper has neither found theoretical nor empirical evidence that sublook correlation techniques outperform the classical detector based on the image magnitude. [ABSTRACT FROM AUTHOR]

A generalized continuous wave synthetic aperture radar (GCW-SAR) concept is proposed in this paper. By using full-duplex radio frontend and continuous wave signaling, the GCW-SAR system can overcome a number of limitations inherent within the existing SAR systems and achieve high-resolution and wide-swath remote sensing with low-power signal transmission. Unlike the conventional pulsed SAR and the frequency-modulated continuous-wave SAR, the GCW-SAR reconstructs a radar image by directly correlating the received 1-D raw data after self-interference cancellation with predetermined location-dependent reference signals. A fast imaging algorithm, called the piecewise constant Doppler (PCD) algorithm, is also proposed, which produces the radar image recursively in the azimuth direction without any intermediate step, such as range compression and migration compensation, as required by conventional algorithms. By removing the stop-and-go assumption or slow-time sampling in azimuth, the PCD algorithm not only achieves better imaging quality but also allows for more flexible waveform and system designs. Analyses and simulations show that the GCW-SAR tolerates significant self-interference and works well with a large selection of various system parameters. The work presented in this paper establishes a solid theoretical foundation for next-generation imaging radars. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method based on deformable dictionary learning for detecting the regions of change between multitemporal image pairs. We build on our previous work, which constructed a pair of dictionaries. The main shortcoming of this method was its dependence on a large amount of training data. In practice, there is often a shortage of ground-truthed training images, which limits the expression capability of the resulting dictionaries. This paper overcomes this challenge by incorporating the concept of deformation, wherein each atom of a dictionary is no longer a simple image patch, but instead is a flexible image deformation function. This enables the creation of more expressive dictionaries, capable of generalizing to a far greater variety of image patterns, while using a far smaller amount of ground-truthed images for supervised dictionary training. Deformation similarity is employed for patch matching to find the best set of atoms in the difference image (DI) dictionary for reconstructing image patches for a new input DI. Each such atom can be deformed to achieve a better match, thus extending generality while reducing the number of atoms needed in the dictionary. Multiple deformed atoms are weighted and combined to best reconstruct the input DI patch. Then, the same set of deformations and weights is projected to the corresponding atoms in the CD dictionary to obtain the output change-detection map. Experiments in six realistic synthetic aperture radar data sets demonstrate the robustness and efficiency of the proposed method in comparison with five other state-of-the-art methods from the literature. [ABSTRACT FROM AUTHOR]

It has been previously demonstrated that it is possible to perform remote vibrometry using synthetic aperture radar (SAR) in conjunction with the discrete fractional Fourier transform (DFrFT). Specifically, the DFrFT estimates the chirp parameters (related to the instantaneous acceleration of a vibrating object) of a slow-time signal associated with the SAR image. However, ground clutter surrounding a vibrating object introduces uncertainties in the estimate of the chirp parameter retrieved via the DFrFT method. To overcome this shortcoming, various techniques based on subspace decomposition of the SAR slow-time signal have been developed. Nonetheless, the effectiveness of these techniques is limited to values of signal-to-clutter ratio ≥5 dB. In this paper, a new vibrometry technique based on displaced-phase-center antenna (DPCA) SAR is proposed. The main characteristic of a DPCA-SAR is that the clutter signal can be canceled, ideally, while retaining information on the instantaneous position and velocity of a target. In this paper, a novel method based on the extended Kalman filter (EKF) is introduced for performing vibrometry using the slow-time signal of a DPCA-SAR. The DPCA-SAR signal model for a vibrating target, the mathematical characterization of the EKF technique, and vibration estimation results for various types of vibration dynamics are presented. [ABSTRACT FROM AUTHOR]

Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance information as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete deforestation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross-pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency’s ALOS-1/2 and NASA-ISRO’s NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) image classification is a vital application in remote sensing image processing. In general, PolSAR image classification is actually a high-dimensional nonlinear mapping problem. The methods based on sparse representation and deep learning have shown a great potential for PolSAR image classification. Therefore, a novel PolSAR image classification method based on multilayer projective dictionary pair learning (MDPL) and sparse auto encoder (SAE) is proposed in this paper. First, MDPL is used to extract features, and the abstract degree of the extracted features is high. Second, in order to get the nonlinear relationship between elements of feature vectors in an adaptive way, SAE is also used in this paper. Three PolSAR images are used to test the effectiveness of our method. Compared with several state-of-the-art methods, our method achieves very competitive results in PolSAR image classification. [ABSTRACT FROM PUBLISHER]

Change detection in heterogeneous remote sensing images is an important but challenging task because of the incommensurable appearances of the heterogeneous images. In order to solve the change detection problem in optical and synthetic aperture radar (SAR) images, this paper proposes an improved method that combines cooperative multitemporal segmentation and hierarchical compound classification (CMS-HCC) based on our previous work. Considering the large radiometric and geometric differences between heterogeneous images, first, a cooperative multitemporal segmentation method is introduced to generate multi-scale segmentation results. This method segments two images together by associating the information from the two images and thus reduces the noises and errors caused by area transition and object misalignment, as well as makes the boundaries of detected objects described more accurately. Then, a region-based multitemporal hierarchical Markov random field (RMH-MRF) model is defined to combine spatial, temporal, and multi-level information. With the RMH-MRF model, a hierarchical compound classification method is performed by identifying the optimal configuration of labels with a region-based marginal posterior mode estimation, further improving the change detection accuracy. The changes can be determined if the labels assigned to each pair of parcels are different, obtaining multi-scale change maps. Experimental validation is conducted on several pairs of optical and SAR images. It consists of two parts: comparison on different multitemporal segmentation methods and comparison on different change detection methods. The results show that the proposed method can effectively detect the changes in heterogeneous images, with low false positive and high accuracy. [ABSTRACT FROM AUTHOR]

The detection of airports using synthetic aperture radar (SAR) images has attracted considerable attention. Traditional methods easily result in inaccurate detection due to the complex scenes and multiplicative speckle noise. Therefore, airport detection from SAR images is still a challenging task. In order to limit the influence of unnecessary and attractive details and noise, we propose a multi-layer abstraction saliency model for airport detection in SAR images in this paper. Specifically, we first obtain airport support regions and superpixels in the first layer. According to the dis-similarity between foreground and background superpixels, airport components are explored by iterative refinement for each airport support region in the second layer. In the third layer, airport adobes are produced by clustering. Based on the characteristics of an airport in SAR images, we propose three saliency cues, including local contrast (LC), adobe deformation (AD), and global uniqueness (GU), to obtain adobe-level saliency. Furthermore, we assign saliency to each pixel by Bayesian inference. Finally, we can explore airport location using integrated saliency map. The proposed approach is tested on an airport data set collected from Gaofen-3, TerraSAR, and RadarSat. Our method achieves 88.89% detection rate. The experimental results demonstrate that the proposed algorithm is effective and outperforms the previously airport detection methods. The code will be available at https://github.com/NengyuanLiu/MyAirportSaliency. [ABSTRACT FROM AUTHOR]

Multi-modal data fusion has recently been shown promise in classification tasks in remote sensing. Optical data and radar data, two important yet intrinsically different data sources, are attracting more and more attention for potential data fusion. It is already widely known that a machine learning-based methodology often yields excellent performance. However, the methodology relies on a large training set, which is very expensive to achieve in remote sensing. The semi-supervised manifold alignment (SSMA), a multi-modal data fusion algorithm, has been designed to amplify the impact of an existing training set by linking labeled data to unlabeled data via unsupervised techniques. In this paper, we explore the potential of SSMA in fusing optical data and polarimetric synthetic aperture radar (SAR) data, which are multi-sensory data sources. Furthermore, we propose a MAPPER-induced manifold alignment (MIMA) for the semi-supervised fusion of multi-sensory data sources. Our proposed method unites SSMA with MAPPER, which is developed from the emerging topological data analysis (TDA) field. To the best of our knowledge, this is the first time that SSMA has been applied on fusing optical data and SAR data, and also the first time that TDA has been applied in remote sensing. The conventional SSMA derives a topological structure using $k$ -nearest neighbor (kNN), while MIMA employs MAPPER, which considers the field knowledge and derives a novel topological structure through the spectral clustering in a data-driven fashion. The experimental results on data fusion with respect to land cover land use classification and local climate zone classification suggest superior performance of MIMA. [ABSTRACT FROM AUTHOR]

Recently, deep neural networks have received intense interests in polarimetric synthetic aperture radar (PolSAR) image classification. However, its success is subject to the availability of large amounts of annotated data which require great efforts of experienced human annotators. Aiming at improving the classification performance with greatly reduced annotation cost, this paper presents an active deep learning approach for minimally supervised PolSAR image classification, which integrates active learning and fine-tuned convolutional neural network (CNN) into a principled framework. Starting from a CNN trained using a very limited number of labeled pixels, we iteratively and actively select the most informative candidates for annotation, and incrementally fine-tune the CNN by incorporating the newly annotated pixels. Moreover, to boost the performance and robustness of the proposed method, we employ Markov random field (MRF) to enforce class label smoothness, and data augmentation technique to enlarge the training set. We conducted extensive experiments on four real benchmark PolSAR images, and experiments demonstrated that our approach achieved state-of-the-art classification results with significantly reduced annotation cost. [ABSTRACT FROM AUTHOR]

Studies on the potential of using synthetic aperture radar (SAR) imaging data for remote sensing in oil palm growth needs to be done in depth. In this paper, we utilized a scattering model for oil palm to study and understand how L-band microwave interacts with the structure of the oil palm. The model is developed based on the radiative transfer theory and solved iteratively up to the second order. The knowledge obtained can likely be used to interpret the backscattering behavior of oil palm with respect to growth. The oil palm is modeled as a multilayer dense medium with leaflets as elliptical disk while the trunk and fronds as cylinders. The model takes into consideration of the close spacing effects of scattering in a dense medium with the use of phase correction theory. Furthermore, near-field interactions between closely spaced scatterers are also considered via the Fresnel phase and amplitude corrections. Ground truth data were measured from the oil palm fields and are used as input parameters in the model. Five different age groups of oil palm were chosen and their backscatter coefficient simulated at L-band frequency. L-band SAR data acquired were compared with the simulated results, and it was found that there was a good agreement between the simulated results and measured data. For further understanding, the model also simulated the breakdown of backscatter contribution by various components of oil palm which allows a better understanding of the interaction between microwave backscatter with specific components. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR), the separation of the height between the ground subsidence phase components and the atmospheric phase delay mixed in the global SAR interferometry (InSAR) phase information is an issue of primary concern in the remote sensing community. This paper describes a complete procedure to address the challenge to estimate the atmospheric phase screen and to separate the three-phase components by exploiting only one InSAR image couple. This solution has the capability to process persistent scatterers subsidence maps potentially using only two multitemporal InSAR couples observed in any atmospheric condition. The solution is obtained by emulating the atmosphere compensation technique that is largely used by the global positioning system where two frequencies are used in order to estimate and compensate the positioning errors due to atmosphere parameters’ variations. A sub-chirping and sub-Doppler algorithm for atmospheric compensation is proposed, which allows the successful separation of the height from the subsidence and the atmosphere parameters from the interferometric phase observed on one InSAR couple. The results are given processing images of two InSAR couples observed by the COSMO-SkyMed satellite system. [ABSTRACT FROM AUTHOR]

Coregistration of high-resolution optical and synthetic aperture radar (SAR) images is still an ongoing problem due to different imaging mechanisms of two kinds of remote sensing images. In this paper, we propose an optical flow-based algorithm to solve the dense registration problem [optical-to-SAR (OS)-flow]. Unlike parametric registration methods that estimate a transformation model, OS-flow aims to find pixelwise correspondences between optical and SAR images. Specifically, two frameworks of OS-flow, a global method and a local method, are proposed. Due to the drastic differences between SAR and optical images, two dense feature descriptors, rather than the raw intensities, are utilized to retain the constancy assumption in optical flow estimation. Considering the inherent properties of the two images, two dense descriptors are constructed using consistent gradient computation. After satisfying the constancy assumption, the global method estimates the flow map by optimizing an objective function, and the local method iteratively estimates the flow vector in a local neighborhood. Both methods use the coarse-to-fine matching strategy to address large displacements and reduce the computational cost. Experiments on several optical-to-SAR image pairs in various scenarios show that the proposed methods have a strong ability to match across optical and SAR images and outperform other state-of-the-art methods in terms of registration accuracy. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (SAR) data are inevitably contaminated by polarization crosstalk and channel imbalance, which propagate to the error of final remote sensing product. To ensure the successful estimation of forest heights from forthcoming polarimetric SAR interferometry (PolinSAR) campaigns, a critical study on the polarimetric system requirements of PolinSAR for forest height mapping must be carried out. This paper establishes an analytical model for forest height estimation error including dependences on polarimetric system parameters including crosstalk, channel imbalance, and system noise. Simulation analyses are conducted on the real airborne SAR data acquired by the E-SAR system to validate the proposed theoretical error dependence model. We demonstrate that the established error model can be used not only by the system designers as a guidance for setting the polarimetric system requirements of PolinSAR for forest height mapping, but also by the data analyst to correct for systematic bias in the forest height inversion. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the surface waves investigation and monitoring (SWIM) instrument which will be one of the two payload instruments carried by China France Oceanography SATellite (CFOSAT) with a planned launch date in mid-2018. SWIM is a real aperture wave scatterometer operated at near-nadir incidence angles and dedicated to the measurement of directional spectra of ocean waves. The SWIM flight model is currently being assembled and tested, its performance is being assessed and its prototype data processing algorithm is being developed. The aim of this paper is to provide a complete overview on the motivations and scientific requirements of this mission, together with a description of the design and characteristics of the SWIM instrument, and the analysis of its expected performances based on a prelaunch study. An end-to-end simulator has been developed to evaluate the quality of the data products, thus allowing the overall performance of the instrument to be assessed. Simulations run with two subsets of full orbit subsets show that the performances of the instrument and the inversion algorithms will meet the scientific requirements for the mission. [ABSTRACT FROM PUBLISHER]

Automatic registration of multimodal remote sensing data [e.g., optical, light detection and ranging (LiDAR), and synthetic aperture radar (SAR)] is a challenging task due to the significant nonlinear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the histogram of orientated phase congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first extend the phase congruency model to generate its orientation representation and use the extended model to build HOPCncc. Then, a fast template matching scheme for this metric is designed to detect the control points between images. The proposed HOPCncc aims to capture the structural similarity between images and has been tested with a variety of optical, LiDAR, SAR, and map data. The results show that HOPCncc is robust against complex nonlinear radiometric differences and outperforms the state-of-the-art similarities metrics (i.e., NCC and mutual information) in matching performance. Moreover, a robust registration method is also proposed in this paper based on HOPCncc, which is evaluated using six pairs of multimodal remote sensing images. The experimental results demonstrate the effectiveness of the proposed method for multimodal image registration. [ABSTRACT FROM PUBLISHER]

This paper describes the Naval Research Laboratory Multichannel Synthetic Aperture Radar (NRL MSAR) and presents initial results from the first field deployment of this system. The NRL MSAR is an airborne test bed designed to investigate remote sensing and surveillance applications that exploit multiple along-track phase centers, particularly applications that require measurement of scene motion. The system operates at X-band and supports 32 along-track phase centers through the use of two transmit horns and 16 receive antennas. As illustrated in this paper, SAR images generated with these phase centers can be coherently combined to directly measure scene motion using the velocity SAR algorithm, and these measurements can then be used to correct the image distortion that the motion causes. In September 2014, this unique radar was deployed for the first time on an airborne platform. This paper presents a description of the system and results from its inaugural deployment, including the correction of distorted maritime signatures. These data were collected over an ocean inlet and contain a variety of moving backscatter sources, including automobiles, ships, shoaling ocean waves, and tidal currents. [ABSTRACT FROM PUBLISHER]

This paper presents an electromagnetic modeling of temporal variations of microwave backscatter from rice canopy based on radiative transfer theory to understand the complex microwave scattering mechanisms of rice crops at different growth stages. Model validation is made by a comparison with field measurements from four independent campaigns by ground-based scatterometers and spaceborne synthetic aperture radar (SAR). Then, the frequency responses and angular dependence are examined, followed by an analysis of parameter sensitivity and uncertainty to microwave backscatter. The validation shows promising results for the physically based model in assessing radar response of rice plant in its full-growth stage. It is found that radar scattering of rice canopy is highly dependent on the growing stages of rice plants, in addition to radar configurations, such as frequency, polarization, and incident angle. Moreover, backscattering of rice canopy is more dependent on stems than on leaves, and as rice plants grow, increasing stems and leaves tend to promote HH- and VH-polarized scattering, while first promoting and then reducing VV-polarized scattering. Moreover, the greatest uncertainty takes place at the late growth stage for copolarization and at the early stages for cross-polarization. The main contributions of this paper can be summarized as follows: 1) the establishment of a rice canopy microwave backscattering model using radiative transfer theory adapted to all the stages in the phenological cycle of rice; 2) the validation of the performance of the proposed model with numerous independent measurements from spaceborne SAR and ground-based scatterometers from different countries and regions; 3) the investigation of the interactions between microwave backscatter signatures and rice canopy growth variables over growth stages; and 4) the identification of the dominant influential parameters of the rice scattering model and determination model uncertainty. [ABSTRACT FROM PUBLISHER]

Due to the high-altitude illumination and the separation of the receiver and transmitter, geosynchronous (GEO) spaceborne–airborne bistatic synthetic aperture radar (BiSAR) is more flexible and accessible in remote sensing applications. In this paper, the Doppler characteristics of GEO BiSAR with a squint receiver are analyzed. It is found that the Doppler spectrum is generally aliased in GEO BiSAR regarding the low pulse repetition frequency (PRF) adopted by the GEO SAR. In order to suppress the ambiguity without adjusting the PRF of GEO SAR, the azimuth multichannel receiving technique is applied to the receiver and then the multichannel transfer function for GEO BiSAR is derived. However, the whole bandwidth of the imaging scene is much larger than that of the center point, which requires extra receiving channels to suppress the ambiguity and thereby increasing the system complexity. A two-step nonlinear chirp scaling (NLCS) method is proposed to obtain the well-focused image with reduced receiving channels. First, a preprocessing step is conducted to achieve space-variant range cell migration correction. After that, the first-step NLCS processing is applied to equalize the 2-D space-variant Doppler centroid and thereby the Doppler bandwidth is decreased, i.e., the required number of receiving channels for reconstruction is reduced. Then, the unambiguous spectrum is reconstructed based on the proposed multichannel transfer function. Finally, the second-step NLCS processing is carried out to equalize the 2-D space-variant high-order Doppler parameters and obtain the well-focused image. The simulation results validate the effectiveness of the proposed method. With the proposed two-step NLCS method, the well-focused image for GEO BiSAR is obtained and the required number of receiving channels can be decreased, which is beneficial to reducing the system complexity and hardware cost. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the pulse-to-pulse correlation properties of nadir-looking pulse-limited altimeters, with the objective of determining the effect of the partial correlation of radar echoes transmitted at much higher rate than the conventional pulse repetition frequency (PRF). This is particularly relevant for the Sentinel-6/Jason-CS mission. The pulse-to-pulse echo power autocorrelation shows much shorter decorrelation times toward the trailing edge of the waveform than those observed for range gates close to the leading edge. At high PRFs this creates a significant variability in the statistical properties of the range gates in the 20-Hz multilooked waveforms. By processing an extensive data set of CryoSat-2 Synthetic Aperture Radar mode data in a pseudo-low resolution mode fashion, we determined that despite the fact that at higher PRFs the noise in the estimation of geophysical parameters is reduced, significant sea-state-dependent biases are also introduced during the retracking process, which are particularly relevant for sea surface height and significant wave height. Those biases will need to be appropriately accounted for when integrating Sentinel-6/Jason-CS data in a climatological data record. [ABSTRACT FROM AUTHOR]

Aiming at improving the classification accuracy with limited numbers of labeled pixels in polarimetric synthetic aperture radar (PolSAR) image classification task, this paper presents a graph-based semisupervised deep learning model for PolSAR image classification. It models the PolSAR image as an undirected graph, where the nodes correspond to the labeled and unlabeled pixels, and the weighted edges represent similarities between the pixels. Upon the graph, we design an energy function incorporating a semisupervision term, a convolutional neural network (CNN) term, and a pairwise smoothness term. The employed CNN extracts abstract and data-driven polarimetric features and outputs class label predictions to the graph model. The semisupervision term enforces the category label constraints on the human-labeled pixels. The pairwise smoothness term encourages class label smoothness and the alignment of class label boundaries with the image edges. Starting from an initialized class label map generated based on $K$ -Wishart distribution hypothesis or superpixel segmentation of PauliRGB images, we iteratively and alternately optimize the defined energy function until it converges. We conducted experiments on two real benchmark PolSAR images, and extensive experiments demonstrated that our approach achieved the state-of-the-art results for PolSAR image classification. [ABSTRACT FROM AUTHOR]

In geosynchronous spaceborne–airborne bistatic synthetic aperture radar (GEO-BiSAR) system, the airborne platform achieves high-resolution imaging by passively receiving the signal from the interested scenario. In this paper, the Doppler characteristics of GEO-BiSAR and the individual contribution of the transmitter and the receiver are first analyzed. The airborne receiver is found to be the dominant contributor for the total Doppler bandwidth, which will lead to Doppler spectrum aliasing regarding the low pulse repetition frequency (PRF) adopted by the GEO-SAR. In order to suppress the Doppler ambiguity without adjusting the PRF of GEO-SAR, azimuth multichannel receiving technique is introduced to the airborne receiver. The multichannel transfer function is derived based on the method of series reversion and the spectrum reconstruction algorithm is then modified for multichannel GEO-BiSAR. Moreover, the reconstruction performance is closely related to the corresponding spacing between each channel (i.e., channel configuration). Therefore, the channel configuration design for GEO-BiSAR aims at optimizing the azimuth ambiguity-to-signal ratio with a satisfactory level of signal-to-noise ratio scaling factor by adjusting the channel configuration. The channel configuration design is modeled as a constrained single objective optimization problem (CSOP). Then, a channel configuration design method based on differential evolution and feasibility rule is proposed to solve the CSOP and obtain the channel configuration for the receiver with the optimal reconstruction performance. Finally, simulations results are presented to verify the effectiveness of the proposed method, and characteristics of channel configuration are analyzed in detail, which can be a practical guide for the implementation of multichannel GEO-BiSAR systems. [ABSTRACT FROM AUTHOR]

In this paper, a time series of 33 TerraSAR-X copolarized synthetic aperture radar (SAR) imagery collected in Stripmap mode over the Gulf of Mexico in a wide range of incidence angles and sea-state condition is exploited, together with a theoretical framework based on the X-Bragg scattering model, to analyze the effects of noise, angle of incidence, (AOI) and wind speed on the standard deviation of the copolarized phase difference ($\sigma _{\phi _{c}}$) evaluated over sea surface with and without oil slicks. This large data set represents an unprecedented opportunity to analyze, for the first time, the influence of both SAR acquisition and surface parameters on the broadening of the copolarized phase difference probability density function (pdf), $p_{\phi _{c}}(\phi _{c})$. Experimental results show that the X-Bragg scattering model, here adopted to predict the sea surface $p_{\phi _{c}}(\phi _{c})$ , gives an understanding of the increasing trend of $\sigma _{\phi _{c}}$ with respect to AOI. It is shown that the noise significantly contributes to broaden $p_{\phi _{c}}(\phi _{c})$ over both slick-free and slick-covered sea surface, while the effects of low-to-moderate wind regimes are negligible. In addition, $\sigma _{\phi _{c}}$ exhibits a larger sensitivity to the scene variability, if compared to single-polarization intensity channels, over both slick-free and oil-covered sea surface. This sensitivity is more pronounced at lower AOIs due to the higher noise equivalent sigma zero (NESZ) that affects larger AOIs. [ABSTRACT FROM AUTHOR]

In our changing world, floods are a threat of increasing concern. Within this context, flood mapping is important for both damage assessment and forecast improvement. Due to the suitability of synthetic aperture radar (SAR) for flood mapping, a broad range of SAR-based flood mapping algorithms has been developed during the past years. However, most of these algorithms were presented based on a single test case only and comparisons between methods are rare. This paper presents an in-depth assessment and comparison of the established pixel-based flood mapping approaches, including global and enhanced thresholding, active contour modeling and change detection. The methods were tested on medium-resolution SAR images of different flood events and lakes across the U.K. and Ireland and were evaluated on both accuracy and robustness. Results indicate that the most suited method depends on the area of interest and its characteristics as well as the intended use of the observation product. Due to its high robustness and good performance, tiled thresholding is suited for automated, near-real time flood detection and monitoring. Active contour models can provide higher accuracies but require long computation times that strongly increase with increasing image sizes, making them more appropriate for accurate flood mapping in smaller areas of interest. [ABSTRACT FROM AUTHOR]

Soil moisture is an important component of natural water cycles and plays a crucial role for the healthy development of ecological environment in arid and semiarid areas. Microwave remote sensing techniques are promising to rapidly monitor regional soil moisture. However, major difficulties associated with retrieving soil moisture via microwave remote sensing are attributed to effects of surface roughness and vegetation cover. The objective of this paper is to investigate the potentials of combined roughness parameters and develop a model that mostly relies on the satellite data and requires minimum a priori information for soil moisture inversion over sparse vegetation covered areas through combing Radarsat-2 synthetic aperture radar (SAR) and GF-1 data. For the purpose, the impacts of vegetation on the radar backscattering coefficient were removed by the water-cloud model (WCM) and a new roughness parameter $Zs$ ($Zs = S^{3}/L$ is defined as a combined roughness parameter) was proposed by simulating relationships between surface roughness parameters, soil moisture, and backscattering coefficients using advanced integral equation model (AIEM). On the basis, a coupling model of soil moisture inversion for blown-wind areas of the Uxin Banner in Inner Mongolia, China, was in turn developed with Radarsat-2 HH and VV polarization data, and the soil moisture content (SMC) values for sparse vegetation covered surfaces were retrieved with limited use of in situ roughness parameters. The in situ measurements and satellite data set were used to validate the reliability of the developed model. The results showed that the retrieved soil moisture levels fell below in situ soil moisture levels before the vegetation effect was removed, and the precision of retrieved soil moisture was effectively improved when the vegetation effect was corrected by the WCM, with the root-mean-square error and mean absolute error decreasing from 7.45% and 6.24% to 5.12% and 3.44%, respectively. According to the map of retrieved soil moisture levels generated for the study area, most of SMCs were below 35% in accordance with field observations. These study results can serve as a foundation for monitoring soil moisture and water environments in arid and semiarid regions. [ABSTRACT FROM AUTHOR]

In the last two decades, the use of synthetic aperture radar (SAR) for remote sensing purposes has significantly developed due to improvements in the quality and the availability of the images. Two powerful SAR techniques, namely, polarimetry and interferometry, have further increased the range of applications of the sensed data. Using polarimetry, geometrical properties and geophysical parameters, such as shape, roughness, texture, and moisture content, can be retrieved with considerable accuracy, while interferometric information may be used to extract vertical information with accuracy less than 1 cm. In this paper, the potential of using joint polarimetry and interferometry techniques in SAR data (PolInSAR) for the purpose of SAR image classification is investigated. To achieve this goal, we extend a covariance symmetry detection framework to the PolInSAR scenario. The proposed approach will be shown to be able to exploit the peculiar structures of the covariance matrices of PolInSAR images to discriminate structures within the image. Results using real-SAR data are presented to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a nonlocal interferometric synthetic aperture radar (InSAR) filter with the goal of generating digital elevation models (DEMs) of higher resolution and accuracy from bistatic TanDEM-X strip map interferograms than with the processing chain used in production. The currently employed boxcar multilooking filter naturally decreases the resolution and has inherent limitations on what level of noise reduction can be achieved. The proposed filter is specifically designed to account for the inherent diversity of natural terrain by setting several filtering parameters adaptively. In particular, it considers the local fringe frequency and scene heterogeneity, ensuring proper denoising of interferograms with considerable underlying topography as well as urban areas. A comparison using synthetic and TanDEM-X bistatic strip map data sets with existing InSAR filters shows the effectiveness of the proposed techniques, most of which could readily be integrated into existing nonlocal filters. The resulting DEMs outclass the ones produced with the existing global TanDEM-X DEM processing chain by effectively increasing the resolution from 12 to 6 m and lowering the noise level by roughly a factor of two. [ABSTRACT FROM AUTHOR]

$L_{1}$ regularization is used for finding sparse solutions to an underdetermined linear system. As sparse signals are widely expected in remote sensing, this type of regularization scheme and its extensions have been widely employed in many remote sensing problems, such as image fusion, target detection, image super-resolution, and others, and have led to promising results. However, solving such sparse reconstruction problems is computationally expensive and has limitations in its practical use. In this paper, we proposed a novel efficient algorithm for solving the complex-valued $L_{1}$ regularized least squares problem. Taking the high-dimensional tomographic synthetic aperture radar (TomoSAR) as a practical example, we carried out extensive experiments, both with the simulation data and the real data, to demonstrate that the proposed approach can retain the accuracy of the second-order methods while dramatically speeding up the processing by one or two orders. Although we have chosen TomoSAR as the example, the proposed method can be generally applied to any spectral estimation problems. [ABSTRACT FROM AUTHOR]

Crop-type classification is one of the most significant applications in polarimetric synthetic aperture radar (PolSAR) imagery. As a remote sensing technique, PolSAR has been proved to have the ability to provide high-resolution information of illustrated objects. However, single-temporal PolSAR data are restricted to provide sufficient information for crop identification due to the complicated condition of varying morphology within various growing stages. With an increasing number of spaceborne PolSAR systems launched, a large amount of real PolSAR data are being generated and used to provide great opportunities for multitemporal analysis. The main contribution of this paper is to improve crop classification accuracy with various features of classical $H$ / $\alpha$ parameters. First, in order to deal with dual-PolSAR data, $H$ / $\alpha$ decomposition algorithm for quad-PolSAR is modified to suit to the case of dual polarization. Second, according to the differential scattering characteristics of main crops, a new parameter is innovatively defined to measure the differential characteristics in the $H$ / $\alpha$ classification plane. Third, crop types are discriminated by applying a supervised classification method with the newly defined parameter. Furthermore, the correctness of the parameter is verified with simulated and real Sentinel-1 data as well as AirSAR data. Finally, the performances of the classification method are investigated by the comparison with complex Wishart, Freeman–Wishart, and support vector machine (SVM) classifiers. Hence, the experimental results show that the proposed method and SVM classifier with the newly defined parameter have the ability to improve crop classification accuracy. [ABSTRACT FROM AUTHOR]

Speckle inherent in synthetic aperture radar (SAR) images usually complicates visual interpretation and brings difficulty to information extraction for applications. Current speckle filters are mainly developed for single SAR image or an image pair (InSAR or PolInSAR). Although some multichannel filters are proposed, they only exploit pixel intensity to identify statistically homogeneous pixels (SHPs). In this paper, we present a new unified approach to filter multitemporal SAR images by adaptively estimating complex covariance matrix-based multitemporal filtering, named CCM-MTF. The key idea is to employ generalized likelihood ratio (GLR) test on the Wishart distributed initial CCM to evaluate the similarity between two pixels. A special design is given to the initial CCM estimation, in which temporal samples are used instead of spatially neighboring samples. Then, a threshold determined by the asymptotic distribution of the logarithm of GLR test statistics at a fixed significance level is used to select spatial SHPs for the reference pixel. Subsequently, the filtering is implemented by estimation of the final CCM from original SAR scattering vector over SHP pixels, and all filtered target information channels including intensity, interferometric phase, and coherence can be explicitly derived from the final CCM. The effectiveness of the proposed CCM-MTF method is validated by experiments on both simulated and real multitemporal SAR images. Both qualitative and quantitative comparisons between CCM-MTF and four state-of-the-art SAR filters are carried out to demonstrate its advantages in terms of speckle suppression as well as detail preservation for all the three information channels. [ABSTRACT FROM AUTHOR]

This paper concerns two polarization orientation parameters of built-up areas derived from the polarimetric synthetic aperture radar (PolSAR) data considering two modeling orthogonal dihedral structures. The first orientation parameter is derived from the well-known circular polarization algorithm with the enrichment of arc distance median filtering using an adaptive neighborhood. The derivation of the second orientation parameter is realized by combining the slope-induced changes in polarimetric orientation angle with the shape-from-shading technique. The combination provides the possibility to measure the incidence angle and the azimuth component of the terrain slopes from the cross-pol SAR intensity image. With reference to the cross scattering model, a doubled cross scattering model (DCSM) is introduced by incorporating the orientation parameters, thus serving to guide the model-based decomposition. Using the DCSM refines the estimation of the cross-pol component by enabling us to further reveal the scattering characteristics of built-up areas. Following a novel criterion, the decomposition is implemented at two layers: one for urban areas and one for nonurban areas. The performance of parameter derivation is demonstrated and evaluated with airborne synthetic aperture radar, uninhabited aerial vehicle synthetic aperture radar, and GF-3 fully PolSAR data over different test sites. The decomposed results are consistent with the reference information provided by the National Land Cover Database 2011 about the land cover classification of test sites and encourage the use of the proposed decomposition scheme for different applications. [ABSTRACT FROM AUTHOR]

Remote sensing technology is an essential link in the global monitoring of the ocean surface, and radars are efficient sensors for detecting marine pollution. When used operationally by authorities, a tradeoff must usually be made between the covered area and the quantity of information collected by the radar. To identify the most appropriate imaging mode, a methodology based on receiver operating characteristic curve analysis has been applied to an original data set collected by two airborne systems operating at L-band, both characterized by a very low instrument noise floor. The data set was acquired during controlled releases of mineral and vegetable oil at sea. Various polarization-dependent quantities are investigated, and their ability to detect slick-covered areas is assessed. A relative ordering of the main polarimetric parameters is reported in this paper. When the sensor has a sufficiently low noise floor, HV is recommended because it provides the strongest slick–sea contrast. Otherwise, VV is found to be the most relevant parameter for detecting slicks on the ocean surface. Among all the investigated quad-polarimetric settings, no significant added value compared to single-polarized data was found. More specifically, it is demonstrated, by increasing the instrument noise level, that the studied polarimetric quantities which combine the four polarimetric channels have performances of detection mainly driven by the instrument noise floor, namely, the noise equivalent sigma zero. This result, obtained by progressively adding noise to the raw synthetic aperture radar (SAR) data, indicates that the polarimetric discrimination between clean sea and polluted area results mainly from the differentiated behavior between single-bounce scattering and noise. It is thus demonstrated, using SAR data collected with a low instrument noise floor, that there is no deviation from Bragg scattering for radar scattering from ocean surface covered by mineral and vegetable oil. [ABSTRACT FROM AUTHOR]

NASA's Soil Moisture Active Passive (SMAP) mission objective is global mapping of surface volumetric soil moisture at 10-km resolution every two to three days and with accuracy of 0.04 cm3 cm−3 (one sigma). In order to achieve this resolution and accuracy, the SMAP utilizes L-band radar and L-band radiometer measurements. The instruments share a rotating 6-m mesh reflector antenna that scans across a 1000-km swath in order to meet the required data refresh rate. The Level-2 Active–Passive soil moisture product (L2_SM_AP) at 9 km is retrieved from the disaggregated/downscaled brightness temperature obtained by merging of active and passive L-band observations. The baseline L2_SM_AP algorithm disaggregates the coarse-resolution (∼36 km) brightness temperatures of the SMAP L-band radiometer using the high-resolution (∼3 km) backscatter data from the SMAP L-band radar with unfocused synthetic aperture processing. The inversion of brightness temperature to estimate surface soil moisture is more mature when compared with inversions of radar backscatter. This is the primary driver of the brightness temperature disaggregation approach to the combined active–passive surface soil moisture product. Furthermore, this approach allows some consistency with the coarse-resolution radiometer-only surface soil moisture product since the disaggregated brightness temperatures sums to the radiometer measurement. The disaggregated brightness temperature contains instrument errors (∼0.7 dB for co-pol backscatter and ∼1.0 dB for cross-pol backscatter, and ∼1.3 K in brightness temperature) inherent in the radar and radiometer. Furthermore, the algorithm has two critical parameters that add uncertainty. Finally, correction of the land brightness temperature (used in the inversion) for water body contributions is a source of uncertainty. In this paper, we introduce analytical expressions for the SMAP downscaled brightness temperature due to all these sources of uncertainty. The expressions allow estimation of uncertainty (in kelvin) for each data granule of the SMAP L2_SM_AP product. Since the uncertainties depend on the given ground conditions, e.g., existing water body fraction and local algorithm parameters that depend on vegetation cover and landscape heterogeneity, it is necessary to evaluate the uncertainty for each data granule. In this paper, we show that the uncertainty expressions closely match Monte Carlo simulations with an overall difference of only ∼0.1 K. Whereas Monte Carlo estimates of uncertainty can only be afforded for a nominal case (such as those typically reported in Algorithm Theoretical Basis Documents as uncertainty tables), the analytical expressions allow uncertainty estimates for every data granule. The expressions are now used to provide uncertainty standard deviation of downscaled brightness temperature at 9 km in the SMAP L2_SM_AP product. These standard deviations are useful for the following: 1) guidance on the expected level of error in the estimate brightness temperature due to the downscaling process and 2) observation error in direct radiance data assimilation. [ABSTRACT FROM PUBLISHER]

The eigendecompostion of the coherency matrix and the related parameters, i.e., entropy $H$, $\alpha$, and anisotropy $A$, are effective and popular tools for the analysis and quantitative estimation of the physical parameters of polarimetric synthetic aperture radar images. However, the speckle noise constitutes the main obstacle that hinders these goals and should be filtered. In this paper, based on studies obtained from extensive simulated data sets, we tried to determine how this noise is transmitted to sample eigendecomposition parameters. The dependence between means and variances of sample parameters leads to building their speckle models and to the definition of a bias elimination procedure. We found that sample eigenvalues were affected by multiplicative noise, whereas sample entropy and sample anisotropy were affected by a mixture of multiplicative and additive noise sources. In addition to its versatility, independence to knowledge of the equivalent number of looks and high ability to bias compensation, the proposed bias suppression technique reduced the variance of noise. Simulated and real data as well as the existing theories are used for validation in this paper. [ABSTRACT FROM PUBLISHER]

A physical approach to synthesize fractal surfaces for a reliable and controllable use within remote-sensing applications is presented in this paper. In particular, the physical criteria to determine the minimum number of tones of the Weierstrass–Mandelbrot (WM) function needed to adequately synthesize realizations of fractional Brownian motion (fBm) processes are analytically derived. The presented rationale relies on considering, in an appropriate range of scales, the WM function as a spectral sampling of the fBm process, and linking the number of sampling functions to the width of the scale range and to the sampling rate; hence, it is shown how to set the lower and the upper scales according to a given remote-sensing problem. In this paper, the condition for determining the WM wavenumber sampling rate is analytically derived. The method is also applied to the efficient simulation of the synthetic aperture radar signal scattered by natural surfaces. [ABSTRACT FROM PUBLISHER]

Autofocusing synthetic aperture imagery by maximizing a statistical quality metric such as contrast or sharpness is a well-documented approach in both synthetic aperture radar (SAR) literature and synthetic aperture sonar literature. It is most successfully applied in spotlight-mode SAR applications, where the assumption of spatial invariance of the corrupting phase function is strong and expressions for the gradients of various quality metrics with respect to standard error models have been calculated. Examples of application to stripmap imagery often involve sectioning images into small blocks, allowing spotlight algorithms to be patchwise applied. This paper formulates the gradient of the cost function in a manner that is consistent with the stripmap error model, inherently providing solutions that compensate for the spatial variance while simultaneously bypassing the need for subdividing an image or aperture. This paper formulates the stripmap gradient expression in conjunction with a computationally efficient imaging approach to rapidly achieve metric-maximizing solutions. Demonstrations are shown on a widebeam wideband rail-based system experiencing random jitter and on an unmanned-underwater-vehicle-mounted sonar system exhibiting artificially injected crabbing and sway errors. Results indicate that the algorithm is particularly effective at compensating for random and rapidly oscillating navigation and jitter errors, as well as sidelobes introduced by crabbing in arrayed systems. [ABSTRACT FROM PUBLISHER]

This paper develops a model of the synthetic aperture, interferometric satellite radar altimeter echo power, and echo cross-product. The model uses the smallness of the satellite pitch and roll angles, and the limited range of satellite altitude to provide a semianalytical echo model, whose numerical dimensions are limited to two in the synthetic aperture case or three in the interferometric case at large roll angles, making its application to extensive data practical. By not implementing the synthetic beam formation, it is demonstrated that the model recovers the conventional case of a pulse-limited altimeter. A theoretical description of the fluctuations in the multilooked, interferometric cross-product, and the synthetic aperture and pulse-limited powers is given by extending the model to describe correlations between individual looks in each case. The model offers the potential to retrieve ocean-surface parameters from synthetic aperture and pulse-limited altimeter data simultaneously, with obvious application to the new generation of altimeters onboard CryoSat-2, Sentinel-3, and Sentinel-6 during the transition to operational synthetic aperture radar altimetry. [ABSTRACT FROM AUTHOR]

This paper presents the estimation of the ground elevation and canopy top elevation from the data collected by an airborne frequency-modulated continuous waveform profiling radar, Tomoradar. The estimated ground and canopy top elevations are critical for the derivation of reference information for the satellite-borne microwave radar data and the modeling of interaction between microwave radar signal and foliage. The methods of estimating the ground elevation and canopy top elevation from profiling radar are introduced, and the accuracy was evaluated via digital terrain model and Velodyne VLP-16 LiDAR integrated with the Tomoradar. To our knowledge, the ranging radar and the LiDAR data were simultaneously collected for the first time. The evaluation proved that the root-mean-square error (RMSE) of ground level estimation of the developed profiling radar can reach up to 0.33 m. When comparing the estimated canopy top peak elevation between the profile radar data and the LiDAR data, it was found that the side lobes of Tomoradar antenna system may produce undesired canopy backscatters when the size of the canopy gap is comparable to the footprint size of the main lobe, resulting in a higher canopy top elevation measurement from Tomoradar than that from LiDAR. The RMSE of the estimated canopy top peak elevation between two data sets was 0.32 m in the best case and 0.852 m on average. Moreover, the RMSE of point-to-point comparing the entire canopy tops elevation estimated from the data of two active remote sensing systems is 0.799 m after excluding the outliers. [ABSTRACT FROM AUTHOR]

The commonly applied K-distribution to model the synthetic aperture radar image amplitude of the heterogeneous (non-Gaussian) sea surface as the basis for vessel detection has shown deficiencies in practical cases, particularly for spacebased systems. Due to a deviation between the K-probability density function and measured histograms in the tails, even the inclusion of thermal noise is oftentimes not sufficient to cover the range of environments that are expected. As a consequence, virtually all detectors try to reduce the large number of obtained false detections by relying on rather heuristic postprocessing steps. Consequently, they forfeit the crucial property of a constant false alarm rate. This paper proposes a novel statistical sea clutter model that describes the data more accurately, especially in challenging environments and thermal-noise limited cases. This new model stands out through its numerical simplicity, permitting efficient parameter adaptation thereby enhancing robustness and reducing computational complexity. Accordingly, the presented sea data model has the potential to replace the widely adopted K-distribution as model of choice for future operational applications. [ABSTRACT FROM AUTHOR]

The TanDEM-X mission is the first free flying bistatic SAR mission. It has the primary objective to generate within a short time frame a global digital elevation model (DEM) of 10-m absolute vertical accuracy and 2-m relative height accuracy. For that, the whole land mass has been mapped at least twice with different baselines. The success of the mission depends on the accuracy of the final DEM and therefore on the reliability of the phase unwrapping (PU) algorithm. Hence, a robust and versatile PU method, which is in accordance with the acquisition concept, is necessary. This paper presents a new method that combines bistatic high-resolution interferometric data in order to perform an accurate PU on a huge amount of data. The dual-baseline PU correction (DB-PUC) framework addresses this challenge by correcting errors that occurred during the single-baseline PU procedure. It benefits from the additional information available through the differential interferogram and the stereo-radargrammetric phase, which are used to correct region-wise the ambiguity bands of the misestimated unwrapped phases to be less sensitive to noise and possible temporal changes. The multilevel of the DB-PUC approach makes it flexible, computationally efficient, and well adapted to deal with the various PU error scenarios. This framework is used operationally for the processing of the data of the TanDEM-X mission. [ABSTRACT FROM AUTHOR]

Compensating the residual motion error (RME) is very important in airborne interferometric synthetic aperture radar (InSAR). In this paper, the wavelet decomposition and polynomial fitting-based (WDPF) method is proposed for detecting and correcting the RME. Wavelet decomposition with root-mean-square error (RMSE) change ratio-based decomposition scale identification is used to detect the RME from the differential interferogram. Polynomial fitting in combination with robust estimation-based least squares is used to absorb the incidence-angle-dependent and topography-dependent components of the RME. A simulated experiment was conducted to test the proposed WDPF method. High-precision RME (with an RMSE of 0.0375 rad) was obtained, which can meet the requirements of InSAR. Real-data L- and P-band InSAR experiments were also performed to test the WDPF method. The results confirmed that the WDPF method can effectively correct the RME for the interferogram. The RMSE of the estimated digital elevation model (DEM) was reduced from 8.03 to 3.46 m and 8.18 to 3.10 m for the L- and P-band interferograms, respectively. Finally, the effects of the external DEM error and polarization on the RME calibration were investigated. The results indicated that the global InSAR DEM products can fulfill the requirement of differential interferogram generation for the WDPF method, and the multipolarization interferograms can help to reduce the effect of the topographic error phase on RME estimation. [ABSTRACT FROM AUTHOR]

High-resolution scanning radar mapping of the surface is an effective tool for addressing concerns in local environmental and social investigation fields. Regrettably, the azimuth resolution of a scanning radar is constrained by the antenna beamwidth. Multiple super-resolution approaches have been applied to the scanning radar to enhance the azimuth resolution, but they suffer from limited resolution improvement. In this paper, a methodology to derive surface estimates from the scanning radar at an improved azimuth resolution is proposed. We first consider the truncated spectrum by discarding the unreliable frequencies to suppress the noise amplification. Then, based on the iterative adaptive approach (IAA), a novel inverse filtering method is formulated to obtain lower sidelobes and a higher resolution. Finally, by taking advantage of the Fourier property of the steering matrix and the Toeplitz structure of the covariance matrix, we exploit the Gohberg-Semencul representation and the data-dependent trigonometric polynomials to derive a fast IAA (FIAA)-based inverse filtering to mitigate the computational burden. Simulation results and real data processing demonstrate that the proposed FIAA-based inverse filtering outperforms the existing super-resolution approaches in resolution improvement and results in a higher computational efficiency. [ABSTRACT FROM AUTHOR]

Because of flexible geometric configuration and trajectory designation, time-domain algorithms become popular for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) applications. In this paper, a new quasi-polar-coordinate-based fast factorized back-projection (FFBP) algorithm combined with data-driven motion compensation is proposed for miniature UAV-SAR imaging. By utilizing wavenumber decomposition, the analytical spectrum of a quasi-polar grid image is obtained, where the phase errors arising from the trajectory deviations can be conveniently investigated and the phase autofocusing can be compatibly incorporated. Different from the conventional FFBP based on a polar coordinate system, the proposed algorithm operates in a quasi-polar coordinate system, where the phase errors become spacial invariant and can be accurately estimated and easily compensated. Moreover, the relationship between phase errors and nonsystematic range cell migration (NsRCM) is revealed according to the analytical image spectrum, based on which the NsRCM correction is developed to further improve the image focusing quality for high-resolution SAR applications. Promising experimental results from the raw data experiments of miniature UAV-SAR test bed are presented and analyzed to validate the advantages of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

With the application requirement, the technique for change detection based on heterogeneous remote sensing images is paid more attention. However, detecting changes between two heterogeneous images is challenging as they cannot be compared in low-dimensional space. In this paper, we construct an approximately symmetric deep neural network with two sides containing the same number of coupled layers to transform the two images into the same feature space. The two images are connected with the two sides and transformed into the same feature space, in which their features are more discriminative and the difference image can be generated by comparing paired features pixel by pixel. The network is first built by stacked restricted Boltzmann machines, and then, the parameters are updated in a special way based on clustering. The special way, motivated by that two heterogeneous images share the same reality in unchanged areas and retain respective properties in changed areas, shrinks the distance between paired features transformed from unchanged positions, and enlarges the distance between paired features extracted from changed positions. It is achieved through introducing two types of labels and updating parameters by adaptively changed learning rate. This is different from the existing methods based on deep learning that just do operations on positions predicted to be unchanged and extract only one type of labels. The whole process is completely unsupervised without any priori knowledge. Besides, the method can also be applied to homogeneous images. We test our method on heterogeneous images and homogeneous images. The proposed method achieves quite high accuracy. [ABSTRACT FROM PUBLISHER]

Synthetic aperture radar (SAR) remote sensing allows high-resolution imaging independent of weather conditions and sunlight illumination and is therefore very attractive for the systematic observation of dynamic processes on the earth’s surface. Conventional SAR systems are, however, limited in that a wide swath can only be imaged at the expense of a degraded azimuth resolution. This limitation can be overcome by using systems with multiple receive subapertures displaced in along-track, but a very long antenna is required to map a wide swath. If a relatively short antenna with a single aperture in along-track is available, it is still possible to map a wide area: multiple subswaths can simultaneously be imaged using digital beamforming in elevation, but “blind ranges” are present between adjacent swaths, as the radar cannot receive while it is transmitting. Staggered SAR overcomes the problem of blind ranges by continuously varying the pulse repetition interval (PRI). A proper selection of the PRIs, together with moderate oversampling in azimuth, allows an accurate interpolation of the nonuniformly sampled raw data into a uniform grid, so that resampled data can then be focused with a conventional SAR processor. This approach thereby allows high-resolution imaging of a wide continuous swath without the need for a long antenna with multiple subapertures. In this paper, the performance of staggered SAR is thoroughly discussed and novel methods for the evaluation of the range and azimuth ambiguity-to-signal ratios in staggered SAR are proposed. An L-band design example based on a reflector antenna with multiple feeds shows that outstanding ambiguity performance is obtained, provided that data are moderately oversampled in azimuth. As an additional benefit, the energy of range and azimuth ambiguities is spread over large areas: ambiguities therefore appear in the image as a noise-like disturbance rather than localized artifacts. The impact of staggered SAR operation on image quality is furthermore assessed with experiments using real data. As the first step, highly oversampled F-SAR airborne data have been used to generate equivalent staggered SAR data sets and to evaluate the performance for different oversampling factors and interpolation methods. Then, the radar instrument of the German satellite TerraSAR-X has been commanded to acquire data over the lake Constance in staggered SAR mode. Measurements on these data show very good agreement with predictions from simulations. Staggered SAR is currently being considered as the baseline acquisition mode for Tandem-L, a proposal for a polarimetric and interferometric spaceborne SAR mission to monitor dynamic processes on the earth’s surface with unprecedented accuracy and resolution. [ABSTRACT FROM PUBLISHER]

Image reranking is a popular postprocessing method for remote sensing image retrieval (RSIR), which aims at enhancing the initial retrieval performance. In general, it takes either users’ opinions or the relationships between images into consideration to find an optimal reranked list based on the initial retrieved results. In this paper, we present a reranking method for improving RSIR, which is named two-stage reranking (TSR). Suppose the k -nearest neighbors of a query RS image have been obtained by the initial retrieval. The first step of our TSR is to edit these neighbors using the editing scheme. A handful of informative and representative RS images are selected by the active learning algorithm, and their binary labels are provided by the users relative to the query image. Then, a binary classifier is trained using the selected RS images and their labels to classify the rest of the neighbors. Finally, both classification results and rank information in the initial retrieval results are considered to decide which neighbor should be excluded. In the next step, the remaining RS images are reranked by the proposed reranking scheme, i.e., multisimilarity fusion reranking. Both the user’s experience and image relationships are taken into account in TSR to ensure the performance of the reranking. The efficiency and the robustness of our method are validated by experiments conducted on two different types of RS images. Compared with the existing visual reranking approaches, our method achieves improved performance. [ABSTRACT FROM PUBLISHER]

The concept of the “velocity synthetic aperture radar” (VSAR)—a multiaperture sensor capable of measuring radial velocities in the scene and utilizing this information to correct motion-induced imaging distortions inherent to SAR—was proposed two decades ago. Lately, with the emergence of truly multichannel systems featuring antenna arrays with dozens of elements, the approach has been enjoying a renewed interest. The viability and effectiveness of the algorithm were successfully demonstrated in a series of airborne field campaigns that involved imaging both man-made targets and natural maritime features. These experiments and the wealth of resulting data also underscored the need for comprehensive mathematical descriptions of expected target signatures in the collected “image stacks” and for further refinements of the VSAR imaging theory. This paper addresses both tasks by building upon the available mathematical results developed for the along-track interferometric SAR imagery of distributed evolving targets. The approach allows simultaneous accounting for all essential effects known to impact SAR imagery of a target or an extended feature: its azimuth velocity, radial velocity and acceleration, as well as finite coherence time. The emphasis is on obtaining closed-form expressions that could readily illustrate the structure and behavior of the VSAR stack spectrum of such a target and help gauge anticipated focusing improvement stemming from the VSAR image correction. In particular, it is rigorously shown that the VSAR algorithm is successful in situations when SAR defocusing arises predominantly from radial motion and short coherence times—the resulting resolution is generally no worse than that of the corresponding real-aperture radar. On the other hand, strong defocusing due to azimuth translation may be problematic to compensate within the VSAR approach framework. [ABSTRACT FROM PUBLISHER]

Synthetic aperture radar (SAR) in along-track interferometry (ATI) mode has been extensively applied in velocity measurements of ocean currents and ship detection. A notch filter was recently proposed and was demonstrated to be a promising tool for ship detection exploiting quad- or dual-polarization SAR information. In this paper, the investigation of the notch filter is extended to the dual-channel ATI-SAR mechanism for ship detection on the ocean surface. First, a theoretical proof that the interferometric magnitude performs better in ship detection than single-channel amplitude/intensity is given based on the signal-clutter-ratio improvement, which validates the advantages of interferometric SAR against conventional single-channel SAR for the purpose of ship observations on the ocean surface. Second, based on the proof, the version of the notch filter that is suitable for the ATI-SAR, that is, the interferometric notch filter (INF), is proposed. We then analyze the statistical models of the INF distance, which can facilitate the adaptive realization of INF for ship detection in ATI-SAR. Finally, the experimental results for dual-channel ATI-SAR data measured by NASA/JPL L-band AIRSAR verify the accuracy of the theoretical analysis and effectiveness of the INF. [ABSTRACT FROM PUBLISHER]

This paper presents an opportunity to cancel range ambiguities in synthetic aperture radar (SAR) configuration. One of the limitations of SAR systems is the range ambiguity phenomenon that appears with long delayed echoes. The reflected signal corresponding to one pulse is detected when the radar has already transmitted the next pulse. Thus, this signal is considered as an echo from the next pulse. This paper investigates the opportunity of coding the transmitted pulses using an orthogonal frequency-division multiplexing pulse. The results show that coded-OFDM signals outperform conventional chirp signal and make it possible to relax constraints placed upon the pulse repetition frequency. [ABSTRACT FROM PUBLISHER]

Many internal wave-wave interaction patterns have been observed in satellite images. However, very few studies have been made on understanding these patterns. Internal wave interactions may result in exceptionally large amplitudes in the interaction zone, which, in turn, pose threats to underwater structures. In this paper, we analyze the characteristics of interaction patterns observed in satellite images of the Mid-Atlantic Bight, such as internal wave phase shifts and amplitude changes. Based on these characteristics, we categorize the patterns into four different types: Mach interaction; regular interaction with prominent positive phase shifts and an amplitude decrease in the interaction zone; regular interaction with prominent negative phase shifts and an amplitude increase in the interaction zone; and wave interactions without phase shifts. We provide a detailed analysis of one observed interaction pattern within each category and compare our findings with existing analytical and numerical models for a two-soliton interaction. One important result from this study of interaction patterns is that the patterns alone can be used to deduce how the amplitude changes in the potentially hazardous interaction zone. This paper thus proves that high-resolution satellite images can provide a useful and efficient means of studying internal wave interaction. [ABSTRACT FROM PUBLISHER]