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Following calibration, hyperspectral images remain affected by spatial dimension nonuniformity, i.e., stripe noise, due to stray light interference, slit contamination, and instrument instability. The full spectrum airborne hyperspectral imager (FAHI) is a Chinese next-generation pushbroom sensor with a spectral range covering the visible near-infrared, shortwave-infrared (SWIR), and thermal infrared regions with spectral sampling intervals of 2.34, 3, and 32 nm, respectively. However, the residual stripe noise remains in FAHI images after relative radiometric correction based on the laboratory calibration, especially in low signal-to-noise ratio bands. To solve this problem, a new technique combining image statistics and spatial filtering algorithms has been developed for FAHI image correction. In this method, image statistics are obtained to calculate the gain and offset of each pixel for image nonuniformity correction. Then, a spatial filter removes the residual stripes. This paper presents the principles of this method along with details of validation experiments and results. To validate the effectiveness of the proposed method, comparison with two destriping methods and quantitative analyses are carried out. Moreover, the method is applied to an SWIR hyperspectral image from the TianGong-1 spacecraft, yielding good results. The experimental results suggest that the proposed method is convenient and practical for improving the relative radiometric accuracy of airborne/spaceborne hyperspectral images. [ABSTRACT FROM AUTHOR]

Many hyperspectral anomaly detectors are designed based on the traditional Mahalanobis distance-based RX algorithm, which is usually considered as an inverse operation of the principal components analysis. Such detectors include the uniform target detector (UTD) algorithm, RX-UTD algorithm, and so on. However, the possibility of background statistical contamination caused by anomalies still exists. In order to alleviate this problem, in this paper, we propose a spectral matched filter (background-anomaly component projection and separation optimized filter) to minimize the average output energy of separate image components and the output values of the weighted background regular term for hyperspectral image anomaly detection, which could strengthen the separation between anomalies and backgrounds. By calculating the optimal solution to the background-anomaly component projection and separation function, we obtain the optimal projection, where we can effectively suppress the background while highlighting the anomalies. Proposed algorithm has the following research advantages: 1) it creates a novel collaborative component projection and robust background optimization function to separate the background and anomalies and 2) it analyzes the intrinsic statistical distribution of pixels and applies appropriate iterative shrinkage-thresholding algorithm to solve the $\ell _{1}$ -min problem. Experiments were conducted on three real hyperspectral data sets. The detection results demonstrate that the proposed algorithm is superior to other state-of-the-art anomaly detection algorithms. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral–spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu’s method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates. [ABSTRACT FROM PUBLISHER]

This paper develops distortion metrics for compressed synthetic aperture radar (SAR) imagery from change detection test statistics. These metrics are used to predict lossy image compression’s impact on change detection performance. The metrics do not require the intended change detection comparison image to provide these benefits. An SAR compression system leveraging the distortion metrics is proposed. The system generates a bad-pixel mask highlighting potential false alarms that are generated due to compression and are subsequently discarded in the change detection process. The proposed system’s performance is demonstrated through noncoherent change detection analysis after JPEG2000 and JPEG image compression. Similarly, a coherent change detection system is evaluated after JPEG2000 image compression. For noncoherent change detection at large compression ratios (CRs) using JPEG2000, the proposed system provides a 33% reduction in false alarms at a 0.1 probability of detection as well as the ability to maintain near-distortionless false alarm rates across a wide range of CRs. At a 0.1 probability of detection for coherent change detection, the system provides a 37% reduction in false alarms at modest CRs. The coherent change detection system is also demonstrated to maintain low false alarm rates across a range of CRs. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the constant false-alarm rate (CFAR) property of the RX anomaly detector which is widely used for the analysis of hyperspectral data. The RX detector relies on an adaptive scheme where the mean vector and the covariance matrix of the background are locally estimated from the image pixels themselves. First, demeaning is accomplished by removing the estimated local background mean value, and then, the covariance matrix is estimated in a homogeneous neighborhood of each pixel. In principle, if the local mean is perfectly removed and the covariance matrix is estimated from background pixels sharing the same covariance matrix, the RX algorithm has the CFAR property, which is highly desirable in practical applications. The CFAR behavior of the algorithm also requires the spatial stationarity of the random noise affecting the hyperspectral image. In data collected by new-generation sensors, such an assumption is not valid because photon noise contribution, which depends on the spatially varying signal level, is not negligible. This has motivated us to analyze the behavior of the RX algorithm with respect to the CFAR property in data affected by signal-dependent (SD) noise. In this paper, we show both theoretically and experimentally that the SD noise is one of the causes of the non-CFAR behavior of the RX detector that we have experienced in many practical situations. We propose a strategy to enhance the robustness of the anomaly detection scheme with respect to the CFAR property based on an adaptive nonlinear transform aimed at reducing the dependence of the noise on the signal level. Experiments on simulated data and real data collected by a new hyperspectral camera are also presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper, a fast a priori knowledge-based ground moving target indication and parameter estimation algorithm applicable to single- as well as to multichannel synthetic aperture airborne radar data is presented. The algorithm operates directly on range-compressed data. Only the intersection points of the moving vehicle signals with the a priori known road axes, which are mapped into the range-compressed data array, are evaluated. For moving vehicle detection and parameter estimation, basically only a single 1-D fast Fourier transformation has to be performed for each considered road point. Hence, the required computational power is low, and the algorithm is well suited for real-time traffic monitoring applications. The proposed algorithm enables the estimation of the position and velocity vectors of detected moving vehicles independent of the number of channels. A single-channel synthetic aperture radar system may be sufficient in case of fast moving vehicles. The paper includes a detailed performance assessment together with experimental results that demonstrate the applicability in a real-world scenario. [ABSTRACT FROM AUTHOR]

This paper deals with subspace-based target detection in hyperspectral images. Specifically, it focuses on a general detection scheme where, first, background is suppressed through orthogonal-subspace projection and then target detection is accomplished. An adequate estimation of the background subspace is essential to a successful outcome. The background subspace has been typically estimated globally. However, global approaches may be ineffective for small-target-detection applications since they tend to overestimate the background interference affecting a given target. This may result in a low target residual energy after background suppression that is detrimental to detection performance. In this paper, we propose a novel and fully automatic algorithm for local background-subspace estimation (LBSE). Local background has typically a lower inherent complexity than that of global background. By estimating the background subspace over a local neighborhood of the test pixel, the resulting background-subspace dimension is expected to be low, thus resulting in a higher target residual energy after suppression which benefits the detection performance. Specifically, the proposed LBSE acts on a per-pixel basis, thus adaptively tailoring the estimated basis to the local complexity of background. Both simulated and real hyperspectral data are employed to investigate the detection-performance improvements offered by LBSE with respect to both global and local methodologies previously presented. [ABSTRACT FROM AUTHOR]

With the continuing increase in the number of images collected everyday from different sensors, the automated registration of multisensor/multispectral images has become a very important issue. This is particularly true when pre- and postevent image comparison is concerned: For this particular application, the requirement of obtaining the earliest possible postevent image imposes the use of data potentially possessing significantly different characteristics with respect to the pre-event image. Strongly inhomogeneous image pairs require robust automatic registration techniques, preferably based on resolutionindependent feature-based registration. In a previous paper, we proposed a mode-based feature-matching scheme mutated from the computer vision domain and adapted to pre- and postevent feature matching. Some of the weak points highlighted in that first version are addressed in this paper, where a new version of the method is proposed, which exploits a new piece of information, i.e., the adjacency between feature points, generally preserved across the disaster event. Extensive generation of synthetic cases allowed one to obtain significant feedback and, consequently, tune the algorithm. Three real cases of pre- and postevent feature matching on high-resolution satellite images are shown and discussed. [ABSTRACT FROM AUTHOR]

The combination of frequency-modulation continuous-wave (FMCW) technology and synthetic aperture radar (SAR) promises a lightweight, cost-effective, and high-quality imaging sensor for remote sensing. However, the long signal duration time leads to the failure of the conventional start/stop approximation of the pulsed SAR. In this paper, a signal model is proposed to address the effects of the continuous motion during the transmit time on the echoed signal. Based on the model, an analytical point target reference spectrum is derived. From the spectrum, it will be seen that the continuous motion introduces an additional range-azimuth coupling term and a range walk term compared with the conventional pulsed SAR. The range walk term is well known, whereas the foregoing range-azimuth coupling term is formulated for the first time in the FMCW SAR community. For the squint and spotlight modes, these range walk and range-azimuth coupling terms might significantly degrade the image quality. In this paper, based on the proposed analytical signal model, we further discuss the application of the wavenumber domain algorithm for the FMCW SAR data. In addition, different approximations of the Stolt mapping are made to highlight the effect of the range-dependent higher-order range-azimuth coupling terms on the 2-D impulse responses. Finally, X-band simulated experiments and Ka-band real FMCW SAR data are used to validate the signal model and the processing method. [ABSTRACT FROM AUTHOR]

This paper departs from the popular usage of the Backus-Gilbert inversion (BGI) method as a tool for inversion of antenna temperature measurements in microwave radiometry. The BGI method is applied in this paper to enhance the information content of an existing set of oversampled brightness-temperature (TB) data. The purpose is to isolate the inversion process from its resolution enhancement counterpart. The advantage gained is that the resolution enhancement can be performed in a simplified way and in a different level of processing that starts with the scan-mode TB data product and simply requires with it the knowledge of the antenna gain pattern and the sensor's scan geometry. The technique is demonstrated with the 19.35-GHz Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) channel, which provides oversampled TB data. The radiometric resemblance of this channel with that of the 37 GHz and geocollocation of their TB footprints facilitate validation of the enhancement of features. The significance of oversampling the low-frequency (LF) radiometer channels is underscored in the process, which gives the authors the confidence to propose oversampling of the LF data for the forthcoming sensor Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) onboard the Megha-Tropiques mission, which is a joint ISRO-CNES collaboration (due for launch in 2009). [ABSTRACT FROM AUTHOR]

The combination of frequency-modulated continuous-wave (FMCW) technology and synthetic aperture radar (SAR) techniques leads to lightweight cost-effective imaging sensors of high resolution. One limiting factor to the use of FMCW sensors is the well-known presence of nonlinearities in the transmitted signal. This results in contrast- and range-resolution degradation, particularly when the system is intended for high-resolution long-range applications, as it is the case for SAR. This paper presents a novel processing solution, which solves the nonlinearity problem for the whole range profile. Additionally, the conventional stop-and-go approximation used in pulse-radar algorithms is not valid in FMCW SAR applications under certain circumstances. Therefore, the motion within the sweep needs to be taken into account. Analytical development of the FMCW SAR signal model, starting from the deramped signal and without using the stop-and-go approximation, is presented in this paper. The model is then applied to stripmap, spotlight, and single-transmitter/multiple-receiver digital-beamforming SAR operational mode. The proposed algorithms are verified by processing real FMCW SAR data collected with the demonstrator system built at the Delft University of Technology. [ABSTRACT FROM AUTHOR]

We describe in detail the theoretical and practical implementation aspects of a simulation for marine radars which can, in particular, be used in multistatic configurations. Since the simulator is intended to deliver pseudoraw signals, it can be used later as a tool to benchmark and improve postprocessing algorithms such as bistatic synthetic aperture radar focusing algorithms and ship wake detection algorithms. The work is divided into two parts. This paper reviews and recalls theoretical prerequisites necessary in implementing such a simulator. Included are the full derivation of the bistatic radar equation from the transmitter to the receiver, accounting also for the transmit-receive time, a description of the sea state phenomenology, a review of the theory of electromagnetic scattering from the sea surface, and the presentation and validation of the method used in the simulation. A companion paper discusses the practical implementation aspects of the simulator as well as an analysis of our results. [ABSTRACT FROM AUTHOR]

In a previous paper, a two-dimensional Fourier domain synthetic aperture radar (SAR) raw signal simulator that exploits the efficiency of fast Fourier transform algorithms was presented. It accounts for the effects of sensor trajectory deviations and is able to generate the raw signal corresponding to extended scenes in a few seconds. However, a narrow-beam—slow-deviation assumption is made; hence, the approach can be applied only to some SAR systems and/or trajectory deviations. To overcome this limitation, in this paper, we show that the narrow-beam—slow-deviation assumption can be relaxed, at the expense of computation efficiency, if use is made of one-dimensional azimuth Fourier domain processing followed by range time-domain integration. The latter approach only requires some reasonable assumptions on the sensor motion and on the SAR system features; hence, it can be used for airborne SAR systems, and turns out to be still much more efficient than the time-domain one; hence, extended scenes can still be considered. Validity limits of the approach are also analytically evaluated, and several simulation results are finally presented to verify the effectiveness of the proposed simulation scheme. [ABSTRACT FROM AUTHOR]

Spatial resolution is a key parameter of all remote sensing satellites and platforms. The nominal spatial resolution of satellites is a well-known characteristic because it is directly related to the area in ground that represents a pixel in the detector. Nevertheless, in practice, the actual resolution of a specific image obtained from a satellite is difficult to know precisely because it depends on many other factors such as atmospheric conditions. However, if one has two or more images of the same region, it is possible to compare their relative resolutions. In this paper, a wavelet-decomposition-based method for the determination of the relative resolution between two remotely sensed images of the same area is proposed. The method can be applied to panchromatic, multispectral, and mixed (one panchromatic and one multispectral) images. As an example, the method was applied to compute the relative resolution between SPOT-3, Landsat-5, and Landsat-7 panchromatic and multispectral images taken under similar as well as under very different conditions. On the other hand, if the true absolute resolution of one of the images of the pair is known, the resolution of the other can be computed. Thus, in the last part of this paper, a spatial calibrator that is designed and constructed to help compute the absolute resolution of a single remotely sensed image is described, and an example of its use is presented. [ABSTRACT FROM AUTHOR]

In this paper, we present a nonlinear version of the well-known anomaly detection method referred to as the RX-algorithm. Extending this algorithm to a feature space associated with the original input space via a certain nonlinear mapping function can provide a nonlinear version of the RX-algorithm. This non- linear RX-algorithm, referred to as the kernel RX-algorithm, is basically intractable mainly due to the high dimensionality of the feature space produced by the nonlinear mapping function. However, in this paper it is shown that the kernel RX-algorithm can easily be implemented by kernelizing the RX-algorithm in the feature space in terms of kernels that implicitly compute dot products in the feature space. Improved performance of the kernel RX-algorithm over the conventional RX-algorithm is shown by testing several hyperspectral imagery for military target and mine detection. [ABSTRACT FROM AUTHOR]

This paper develops a critical review of the hyperspectral splitting of the solar reflected radiation acquired by hyperspectral imaging sensors. The bandwidth used in the range from 2.0-2.5 μm by many hyperspectral sensors sometimes is too narrow for land cover classification. In fact, hyperspectral imagers often suffer from low signal-to-noise (SNR) in the short-wave infrared region of the electromagnetic spectrum, resulting in noisy image collection. This paper presents a new methodological approach to the splitting of the solar reflected radiation, called the "superspectral approach." It is based on the principle of increasing the channel bandwidth by increasing the number of wavelengths, to build synthetic spectral bands with higher SNRI The method- ology has been applied to the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, an airborne hyperspectral scanner used for environmental remote sensing applications in Italy. Interesting results have been achieved in crop classification, processing the Cordenons survey carried out in August 2001 in the northeastern part of Italy. The Spectral Angle Mapper algorithm was used for classification because it is insensitive to shadows. For accuracy assessment, the overall accuracy (OA) and kappa coefficient (k) were calculated and used in the comparison. Using the superspectral approach, an increment in the overall accuracy of about 42% and an increment in the kappa coefficient of about 51 % were obtained in comparison to the classification accuracy of unprocessed original MINIS data (OA = 41.21, k = 0.35). A second case study is presented using the National Aeronautics and Space Administration's experimental hyperspectral imager HYPERION. Data acquired over the lake of Garda (Italy) in October 2002 was processed with the superspectral approach. Comparing the simulated HYPERION superspectral bands with the original data, SNR improvements are achieved in the shortwave infrared region (from 0.7-54.2 for 2.012-μm wavelength and from 0.7-64.5 for the 2.365-μm wavelength). The methodology proposed is sensor independent and can be applied to any of the hyperspectral sensors currently available. [ABSTRACT FROM AUTHOR]

Target detection in nonhomogeneous sea clutter environments is a complex and challenging task due to the capture effect from interfering outliers and the clutter edge effect from background intensity transitions. For synthetic aperture radar (SAR) measurements, those issues are commonly caused by multiple targets and meteorological and oceanographic phenomena, respectively. This paper proposes a segmentation-based constant false-alarm rate (CFAR) detection algorithm using truncated statistics (TS) for multilooked intensity (MLI) SAR imagery, which simultaneously addresses both issues. From our previous work, TS is a useful tool when the region of interest (ROI) is contaminated by multiple nonclutter pixels. Within each ROI confined by the reference window, the proposed scheme implements an automatic image segmentation algorithm, which performs a finite mixture model estimation with a modified expectation–maximization algorithm. Data truncation is applied here to exclude all possible statistically interfering classes, and sample modeling is based upon the truncated two-parameter gamma model. Next, CFAR detection is conducted pixel by pixel, utilizing the statistical information obtained from the segmentation process within the local reference window. The segmentation-based CFAR detection scheme is examined with real Radarsat-2 MLI SAR imagery. Compared with the conventional CFAR detection approaches, our proposal provides improved background clutter modeling and robust detection performance in nonhomogeneous clutter environments. [ABSTRACT FROM AUTHOR]

Different Earth-observing (EO) sensors have various capabilities for diverse observing tasks. Sensor planning services make the choice of web-ready sensors for specific observing tasks with regard to observing requests and sensor capabilities. Sensor capabilities rely on various parameters; thus, choosing EO sensors for specific observing tasks relying directly on these parameters is a multicriteria decision process. A sensor's capability can be drawn from these parameters with the help of an algorithm. Furthermore, if divided into different clusters based on capabilities, applicable sensors can be more easily chosen for a category of observing tasks. In this paper, a spaceborne EO optical sensor static capability index (SSCI) mechanism is drawn from an evaluation-and-clustering algorithm, which is composed of a self-organizing neural map in combination with weighted principal component analysis. The scheme of SSCI relies on no expert analysis system and thus is more flexible and efficient. EO scenarios of disaster reactions are among the application of this algorithm. In particular, scenarios of flooding disaster forecasting, relief aiding, and postdisaster loss assessment within the framework of International Charter on Space and Major Disasters have been utilized for experiments. They have shown that the SSCI assessing algorithm is feasible and stable, and the EO optical sensor clustering algorithm based on SSCI can offer reasonable clustering accuracies of EO optical sensors. In our experiments, the EO optical sensor SSCI computation and clustering algorithm had a time consumption within 2 s and 2 min, respectively, and memory consumption within 200 MB on a normal personal computer. [ABSTRACT FROM PUBLISHER]

This paper describes the principles and implementation of an algorithm for the classification of hyperspectral remote sensing images. The proposed approach is novel and can be included within the category of the spectral–spatial classification algorithms. The elements of novelty of the algorithm are as follows: 1) the implementation of two classifiers that work iteratively, each one exploiting the decision of the other to improve the training phase, and 2) the use of relational features based on the current labeling and on the spatial structure of the image. The two classifiers are fed with the spectral features and with the spatial features, respectively. The spatial features are built using the relative abundance of each class in a neighborhood of the pixel (homogeneity index), where the neighborhood is properly defined. An important contribution to the success of the method is the adoption of a multiclass classifier, the multinomial logistic regression, and a proper use of the posterior probabilities to infer the class labeling and build the relational data. The results of the two classifiers are eventually combined by means of an ensemble decision. The algorithm has been successfully tested on three standard hyperspectral images taken from the Airborne Visible–Infrared Imaging Spectrometer and ROSIS airborne sensors and compared with classification algorithms recently proposed in the literature. [ABSTRACT FROM PUBLISHER]

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the primary instruments in the National Aeronautics and Space Administration Earth observing system. The first MODIS instrument was launched in December, 1999 onboard the Terra spacecraft. MODIS has 36 bands, covering a wavelength range 0.4-14.4 μm. MODIS band 27 (6.72 μm) is a water vapor band, which is designed to be insensitive to Earth surface features. In recent Earth view images of Terra band 27, surface feature contamination is clearly seen with pronounced striping. In this paper, it is shown that these band-27 issues are caused by electronic crosstalk from bands 28-30. An algorithm using a linear approximation is developed to correct the crosstalk effect. The crosstalk coefficients are derived from Terra MODIS lunar observations. They show that the crosstalk is strongly detector-dependent and the crosstalk pattern has changed in a noticeable fashion since launch. The crosstalk contributions were positive to the instrument response of band 27 early in the mission but became negative and much larger in magnitude at later stages of the mission for most detectors of the band. The algorithms are applied to both the black body (BB) calibration and the MODIS L1B calibrated products. With the crosstalk effect significantly removed, the calibration coefficients of Terra MODIS band 27 derived from the BB show that the detector differences become smaller. With the algorithms applied to MODIS L1B products, the Earth surface features are significantly removed, thereby restoring the radiometric balance of the band and substantially reducing the striping features in the image. [ABSTRACT FROM PUBLISHER]

The combination of multiple detectors and a double-sided mirror in the Moderate Resolution Imaging Spectroradiometer (MODIS) introduces striping in the imagery despite the use of onboard calibration systems. This results in a sharp unidirectional pattern across scan lines and leads to significant radiometric uncertainties in level-1 radiances, which further propagate into derived level-2 geophysical products such as the sea surface temperature. Quantifying and minimizing such uncertainties are the key steps in the improvement of remotely sensed data from MODIS. This paper describes a deterministic technique for the estimation of biases due to detector-to-detector miscalibration, mirror banding, and random striping. Using a unidirectional variational model previously introduced for destriping purposes, the scan line noise is first extracted from the observed scenes and then used to evaluate the thermal bias of detectors. The algorithm is applied to Terra and Aqua MODIS thermal emissive bands. Its qualitative performance is illustrated on several representative scenes, and quantitative results of detector biases are reported and discussed. [ABSTRACT FROM AUTHOR]

Among all types of unwanted signals in high-frequency (HF) surface wave (HFSW) over-the-horizon (OTH) radars, radio-frequency interference (RFI) is dominant since HF band is shared by many radio services. In observation data, there are two types of common RFI. The most common one is the conventional RFI which presents vertical stripe paralleling to range axis in range-Doppler spectrum (RDS) and has been exhaustively reported by previous papers. Meanwhile, a new type of RFI characterized by sloping stripes (called \RFISS) in RDS is also frequently observed. This work concentrates on the new \RFISS and establishes a unified model for the above two types of RFI. Based on this generalized model, a time-domain RFI suppression algorithm is proposed here. Benefiting from a closed-form approximate maximum likelihood estimator, the proposed algorithm exhibits excellent performance and is computationally efficient. Its operational performance is evaluated using the field data recorded by experimental HFSW OTH radar of Wuhan University. [ABSTRACT FROM PUBLISHER]

Denoising of hyperspectral imagery in the domain of imaging spectroscopy by conditional random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the strong dependencies across spatial and spectral neighbors have been proved to be very useful. Many available hyperspectral image denoising algorithms adopt multidimensional tools to deal with the problems and thus naturally focus on the use of the spectral dependencies. However, few of them were specifically designed to use the spatial dependencies. In this paper, we propose a multiple-spectral-band CRF (MSB-CRF) to simultaneously model and use the spatial and spectral dependencies in a unified probabilistic framework. Furthermore, under the proposed MSB-CRF framework, we develop two hyperspectral image denoising algorithms, which, thanks to the incorporated spatial and spectral dependencies, can significantly remove the noise, while maintaining the important image details. The experiments are conducted in both simulated and real noisy conditions to test the proposed denoising algorithms, which are shown to outperform the popular denoising methods described in the previous literatures. [ABSTRACT FROM PUBLISHER]

This paper presents a new algorithm, Searchlight, capable of tracking precipitation advection through the complex spatiotemporal sampling pattern of multiplatform low-Earth-orbiting (LEO) passive and active microwave satellite data sets. The algorithm is based on the concept of “shining” a searchlight forward in time along proposed advection velocity vectors from a given LEO overpass. The searchlight “beam” links precipitation patterns in the current satellite overpass to corresponding patterns in multiple future overpasses. A global optimization determines an advection model by maximizing mutual information between linked precipitation measurements. The algorithm has been validated against surface radar over a region covering the Central and Eastern U.S. [ABSTRACT FROM PUBLISHER]

Very high resolution synthetic aperture radar (SAR) sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring flood dynamics in urban areas. In this paper, a hybrid methodology combining backscatter thresholding, region growing, and change detection (CD) is introduced as an approach enabling the automated, objective, and reliable flood extent extraction from very high resolution urban SAR images. The method is based on the calibration of a statistical distribution of “open water” backscatter values from images of floods. Images acquired during dry conditions enable the identification of areas that are not “visible” to the sensor (i.e., regions affected by “shadow”) and that systematically behave as specular reflectors (e.g., smooth tarmac, permanent water bodies). CD with respect to a reference image thereby reduces overdetection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by airborne photography and the very high resolution SAR sensor on board TerraSAR-X highlights advantages and limitations of the method. Even though the proposed fully automated SAR-based flood-mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the mapping capability of high-quality aerial photography. [ABSTRACT FROM PUBLISHER]

Satellite-derived time series of the fraction of absorbed photosynthetically active radiation (fAPAR) are widely used to monitor vegetation dynamics and to detect vegetation anomalies. Several global data sets are available for this purpose. They are produced using different algorithms and/or satellite sensors. This paper compares and analyzes three multitemporal fAPAR data sets derived from SPOT-VEGETATION instrument by explicitly distinguishing between spatial and temporal agreement. The first two data sets are currently used by the Joint Research Centre—Monitoring Agricultural ResourceS Unit (JRC-MARS) for operational yield forecasting and food security assessments. The third time series (named GEOV1) is from a new processing algorithm developed within the European FP7 Geoland2 project. The comparative analysis was conducted for the years 2003 and 2004 over three 10^\circ \times 10^\circ regions with different eco-climatic characteristics (Niger, Brazil, and France). Our study revealed that GEOV1 fAPAR estimates were systematically higher than those of JRC-MARS. The spatial analysis showed moderate to high agreement between data sets with specific seasonality in the three study regions. The temporal agreement showed spatial (and land cover-related) variability spanning from very low to almost perfect. Large differences were observed in regions and periods with large cloud occurrence where GEOV1 provides more reliable and smooth temporal profiles due to improved cloud screening and longer compositing periods. Other sources of disagreement between data sets were identified in differences in the fAPAR retrieval algorithm definitions. [ABSTRACT FROM PUBLISHER]

This paper studies the statistics of clutter data measured with a multichannel space-based synthetic aperture radar (SAR) for the purpose of ground moving target indication (GMTI) and presents an algorithm that implements an adaptive constant false-alarm rate (CFAR) detector. It discusses the differences between airborne and space-based SAR-GMTI clutter measurements and proposes how to develop the statistics of the latter from widely published results for the former. Based upon one of the differences, it proposes an adaptive CFAR detector and demonstrates detection using measured RADARSAT-2 data. The adaptive CFAR algorithm does not require a texture distribution for heterogeneous clutter. [ABSTRACT FROM AUTHOR]

The presence of radio frequency interferences (RFIs) in the data collected by the Soil Moisture and Ocean Salinity (SMOS) mission led by the European Space Agency is larger than what was anticipated, and it has a strong impact on the retrieval of these geophysical parameters. The first efforts to deal with this problem were proposed by DEIMOS Engenharia in the Level 1 Prototype Processor released in November 2010, and they are also a part of its latest versions. DEIMOS focused on three key aspects to detect the RFIs, to mitigate these sources, and to flag the data in SMOS products affected by RFIs. The detection algorithm is based on a statistic approach that computes the most probable position of the RFI source based on multiangular observations made by SMOS. The mitigation algorithm has been designed taking as reference the existing technique to remove the sun effect. The mechanism to flag the data has been developed in cooperation with the European Space Agency and consists in flagging geolocated pixels according to the strength of the RFI. In this paper, three algorithms will be presented, as well as their results. For completion, a high-level description of the data processing for SMOS is also a part of this text. [ABSTRACT FROM AUTHOR]

In several application domains (e.g., crop conversion subsidies, forestry, natural hazard mapping, and spatial planning), the ultimate operational objective of change-detection analysis is actually limited to the identification of only one (or few) specific land-cover transition(s) of interest (i.e., a "targeted" change detection problem), disregarding all the other potential changes occurring in the area under analysis at the same time. Supervised change-detection techniques generally represent the most accurate methodological solution for mapping land-cover changes while identifying the associated land-cover transitions between two different dates. However, the application of these techniques depends on the availability of exhaustive ground-truth information for all the land-cover classes present in the area of interest at the times under investigation. Such a requirement is seldom satisfied since gathering a reliable ground truth for all the classes characterizing the considered scenes at the two dates under analysis presents several practical drawbacks and limitations (both in terms of time and economic cost) that may render this task almost impossible in most real-life cases. Nevertheless, to solve these specific types of problems, it would be highly beneficial for an operator to rely on a robust automatic technique that may allow an effective detection of the "targeted" land-cover transitions by taking into account only ground-truth information for the few classes of interest at each date (thus, avoiding the burden and cost associated to the collection of a full and exhaustive ground-truth data set at both times). In this paper, we address this challenging issue and propose a novel technique (formulated in terms of a compound decision problem) capable of identifying specific "targeted" land-cover transitions by exploiting the ground truth available only for the classes of interest at the two dates, while providing accuracies comparable to those of traditional fully supervised change-detection methods. The proposed technique relies on a partially supervised approach that jointly exploits the expectation-maximization algorithm and an iterative labeling strategy based on Markov random fields accounting for spatial and temporal correlation between the two images. Moreover, the proposed method is applicable to images acquired by different sensors (or to different sets of features) at the two investigated times. Experimental results on different multitemporal and multisensor data sets confirmed the effectiveness and the reliability of the proposed technique. [ABSTRACT FROM AUTHOR]

Images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua exhibit strong detector striping. This artifact is common to most pushbroom scanners and affects both visual interpretation and radiometric integrity of remotely sensed data. A considerable effort has been made to remove stripe noise and reduce its impact on high-level products. Despite the variety of destriping algorithms proposed in the literature, complete removal of stripes without signal distortion is yet to be overcome. In this paper, we tackle the striping issue from a variational angle. Basic statistical assumptions used in previous techniques are replaced by a much realistic geometrical consideration on the striping unidirectional variations. The resulting algorithm is tested on Aqua and Terra MODIS data contaminated with severe stripes and is shown to provide optimal qualitative and quantitative results. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm for striping noise reduction in hyperspectral images is proposed. The new algorithm exploits the orthogonal subspace approach to estimate the striping component and to remove it from the image, preserving the useful signal. The algorithm does not introduce artifacts in the data and also takes into account the dependence on the signal intensity of the striping component. The effectiveness of the algorithm in reducing striping noise is experimentally demonstrated on real data acquired both by airborne and satellite hyperspectral sensors. [ABSTRACT FROM AUTHOR]

Target detection is a key issue in processing hyperspectral images (HSIs). Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. In most cases, both the target spatial distributions and the spectral signatures are unknown, so each pixel is separately tested and appears as a target when it significantly differs from the background. In this paper, we propose two algorithms to improve the signal-to-noise ratio (SNR) of hyperspectral data, leading to detectors that are robust to noise. These algorithms consist in integrating adaptive spatial/spectral filtering into the adaptive matched filter and adaptive coherence estimator. Considering the HSIs as tensor data, our approach introduces a data representation involving multidimensional processing. It combines the advantages of spatial and spectral information using an alternating least square algorithm. To estimate the signal subspace dimension in each spatial mode, we extend the Akaike information criterion, and we develop an iterative algorithm for spectral-mode rank estimation. We demonstrate the interest of integrating the quadtree decomposition to perform an adaptive 3-D filtering and thereby preserve the local image characteristics. This leads to a significant improvement in terms of denoised tensor SNR and, consequently, in terms of detection probability. The performance of our method is exemplified using simulated and real-world HYperspectral Digital Imagery Collection Experiment images. [ABSTRACT FROM AUTHOR]

Preservation of spectral information and enhancement of spatial resolution are regarded as important issues in remote sensing satellite image fusion. In previous research, various algorithms have been proposed. Although they have been successful, there are still some margins of spatial and spectral quality that can be improved. In addition, a new method that can be used for various types of sensors is required. In this paper, a new adaptive fusion method based on component substitution is proposed to merge a high-spatial-resolution panchromatic (PAN) image with a multispectral image. This method generates high-/low-resolution synthetic component images by partial replacement and uses statistical ratio-based high-frequency injection. Various remote sensing satellite images, such as IKONOS-2, QuickBird, LANDSAT ETM+, and SPOT-5, were employed in the evaluation. Experiments showed that this approach can resolve spectral distortion problems and successfully conserve the spatial information of a PAN image. Thus, the fused image obtained from the proposed method gave higher fusion quality than the images from some other methods. In addition, the proposed method worked efficiently with the different sensors considered in the evaluation. [ABSTRACT FROM AUTHOR]

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 \m^3/\m^3. The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation. [ABSTRACT FROM AUTHOR]

Whereas overhead infrared imagery shows promise for detecting buried landmines, detection algorithms must deal with the daunting challenge of distinguishing between landmines and clutter objects which frequently possess similar spatial and spectral characteristics to landmines. However, groups of clutter features are rarely related spatially in the same way that groups of mines are related. For this reason, the recognition of minefield patterns in overhead landmine imagery can be useful to the detection of mines in minefields. In this paper, we present a simple method for detecting grid patterns in imagery, discuss means by which the method may be extended to a more general category of patterns, provide a method for the automated prediction of the locations of undetected mines based upon the observed pattern, and finally we discuss applications. Examples are provided using longwave infrared hyperspectral imagery. [ABSTRACT FROM AUTHOR]

Image segmentation is crucial to object-oriented remote sensing imagery analysis. In this paper, a novel texture-preceded segmentation algorithm is proposed for high-resolution remote sensing imagery, in which texture clustering is first carried out as a loose constraint for later segmentation. The algorithm is based on the graph models of region adjacency graph and nearest neighbor graph, which can achieve fast node merging, depending on the global optimum. Here, a combined distance, composed of texture, spectral, and shape features, is established to measure the similarity between nodes and gives the same semantic descriptions for the texture objects. Then, the combined distance is applied to graph models, and the final segmentation result can be obtained iteratively by fast merging. During the merging process, optimal sequence merging interacts with texture clustering to refine the real edges of a texture region. This algorithm cannot only merge the homogeneous texture segments with spectral variability easily but can also detect the real object boundaries well. The experiments on high-resolution imagery show that, in terms of the same number of segments, the proposed algorithm can improve segmentation accuracy by 10%-20% compared to the results obtained by pure spectral features with Definiens Developer software. [ABSTRACT FROM AUTHOR]

To achieve high-resolution 2-D images, through-wall imaging (TWI) radar with ultra-wideband and long antenna arrays faces considerable technical challenges such as a prolonged data collection time, a huge amount of data, and a high hardware complexity. This paper presents a novel data acquisition scheme and an imaging algorithm for TWI radar based on compressive sensing (CS), which states that a signal having a sparse representation can be reconstructed from a small number of nonadaptive randomized projections by solving a tractable convex program. Instead of measuring all spatial-frequency data, a few samples, by employing an overcomplete dictionary, are sufficient to obtain reliable target space images even at high noise levels. Preliminary simulated and experimental results show that the proposed algorithm outperforms the conventional delay-and-sum beamforming method even though many fewer CS measurements are used. [ABSTRACT FROM AUTHOR]

Knowledge of surface emissivity in the thermal infrared (TIR) region is critical for determining the land surface temperature (LST) from remote-sensing measurements. If emissivity is not well determined, it can cause a significant systematic error in obtaining the LST. The main aim of this paper is to compare different methods for measuring accurate land surface emissivity in the field, namely, the Box method and the Temperature and Emissivity Separation (TES) algorithm. Field emissivities were compared with soil spectra from laboratory measurements. Emissivities were measured for the bands of a multispectral radiometer CE312-2 with effective wavelengths at 8.4, 8.7, 9.1, 10.6, and 11.3 μm, similar to the Advanced Spaceborne Thermal Emission and Reflection Radiometer TIR bands, and a wide channel 8-13 μm. The measurements were made at two sites in New Mexico: the White Sands National Monument and an open shrub land in the Jornada Experimental Range. The measurements show that for both sites the emissivities derived with the Box method agree with those derived with the TES algorithm for the 10.6 and 11.3 jμm bands. However, the emissivities for the shorter wavelength bands are higher when derived with the Box method than those with the TES algorithm, with differences ranging from 2% to 7%. The field emissivities agree within 2% with the laboratory spectrum for the 8-13-, 11.3-, and 10.6-μm bands. However, the field and laboratory measurements in general differ from 2.4% to 9% for the shorter wavelength bands, with the larger value most likely caused by variations in soil moisture. [ABSTRACT FROM AUTHOR]

The main focus of this paper is a rigorous development and validation of a novel canonical correlation feature-selection (CCFS) algorithm that is particularly well suited for spectral sensors with overlapping and noisy bands. The proposed approach combines a generalized canonical correlation analysis framework and a minimum mean-square-error criterion for the selection of feature subspaces. The latter induces ranking of the best linear combinations of the noisy overlapping bands and, in doing so, guarantees a minimal generalized distance between the centers of classes and their respective reconstructions in the space spanned by sensor bands. To demonstrate the efficacy and the scope of the proposed approach, two different applications are considered. The first one is separability and classification analysis of rock species using laboratory spectral data and a quantum-dot infrared photodetector (QDIP) sensor. The second application deals with supervised classification and spectral unmixing, and abundance estimation of hyperspectral imagery obtained from the Airborne Hyperspectral Imager sensor. Since QDIP bands exhibit significant spectral overlap, the first study validates the new algorithm in this important application context. The results demonstrate that proper postprocessing can facilitate the emergence of QDIP-based sensors as a promising technology for midwave- and longwave-infrared remote sensing and spectral imaging. In particular, the proposed CCFS algorithm makes it possible to exploit the unique advantage offered by QDIPs with a dot-in-a-well configuration, comprising their bias-dependent spectral response, which is attributable to the quantum Stark effect. The main objective of the second study is to assert that the scope of the new CCFS approach also extends to more traditional spectral sensors. [ABSTRACT FROM AUTHOR]

This paper investigates RGB color composition schemes for hyperspectral imagery display. A three-channel composite inevitably loses a significant amount of information contained in the original high-dimensional data. The objective here is to display the useful information as distinctively as possible for high-class separability. To achieve this objective, it is important to find an effective data processing step prior to color display. A series of supervised and unsupervised data transformation and classification algorithms are reviewed, implemented, and compared for this purpose. The resulting color displays are evaluated in terms of class separability using a statistical detector and perceptual color distance. We demonstrate that the use of the data processing step can significantly improve the quality of color display, whereas data classification generally outperforms data transformation, although the implementation is more complicated. Several instructive suggestions for practitioners are provided. [ABSTRACT FROM AUTHOR]

The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detection methods by formulating them in an adequate high-dimensional feature space. Finally, the presented methodology allows the integration of contextual information and multisensor images with different levels of nonlinear sophistication. The binary support vector (SV) classifier and the one-class SV domain description classifier are evaluated by using both linear and nonlinear kernel functions. Good performance on synthetic and real multitemporal classification scenarios illustrates the generalization of the framework and the capabilities of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present a new variant of the k-nearest neighbor (kNN) classifier based on the maximal margin principle. The proposed method relies on classifying a given unlabeled sample by first finding its k-nearest training samples. A local partition of the input feature space is then carried out by means of local support vector machine (SVM) decision boundaries determined after training a multiclass SVM classifier on the considered k training samples. The labeling of the unknown sample is done by looking at the local decision region to which it belongs. The method is characterized by resulting global decision boundaries of the piecewise linear type. However, the entire process can be kernelized through the determination of the k-nearest training samples in the transformed feature space by using a distance function simply reformulated on the basis of the adopted kernel. To illustrate the performance of the proposed method, an experimental analysis on three different remote sensing datasets is reported and discussed. [ABSTRACT FROM AUTHOR]

The F16 satellite was successfully launched on October 18, 2003, carrying the first Special Sensor Microwave Imager/Sounder (SSMIS) onboard. In this paper, the SSMIS imaging channels 12-18 are intercalibated against the F15 Special Sensor Microwave/Imager (SSM/I) instrument using Simultaneous Conical Overpassing (SCO) observations from both satellites. Results show that the SSMIS antenna temperatures have a mean bias as large as 1-2 K with a maximum of 3 K at 22.235 GHz with respect to F15. It appears that the mean biases at frequencies from 19.35 to 37 GHz do not strongly depend on the region and season, although the biases at the 91.655-GHz channels are slightly variable. The intercalibration analysis also shows that the nonlinearity may be one of the major sources resulting in differences between F15 SSM/I and F16 SSMIS measurements. For improved calibration and for the future SSM/I and SSMIS reprocessing, the SCO data are further utilized to resolve the SSMIS and SSM/I nonlinearity terms using a newly developed calibration algorithm. With the derived nonlinearity correction, the mean biases of the antenna temperatures between FI5 and F16 are significantly reduced. To intercalibrate SSMIS to the same reference as SSM/I, SSMIS imaging channels can also be linearly mapped to the same and similar F15 SSM/I channels using the SCO matchup data. After the linear mapping, SSMIS snow-free land, snow, and sea ice surface emissivities are consistent with those derived from SSM/I. [ABSTRACT FROM AUTHOR]

This paper discusses the application and adaptation of two existing operational algorithms for land surface emissivity (ϵ) retrieval from different operational satellite/airborne sensors with bands in the visible and near-infrared (VNIR) and thermal IR (TIR) regions: 1) the temperature and emissivity separation algorithm, which retrieves ϵ only from TIR data and 2) the normalized-difference vegetation index thresholds method, in which ϵ is retrieved from VNIR data. [ABSTRACT FROM AUTHOR]

In Landsat Thematic Mapper (TM) images, banding due to memory effect (ME) often appears every 16 lines at both sides of a bright object such as a cloud, which not only visually degrades the images but also makes quantitative analysis difficult. The ME is induced by an electric circuit connecting the detectors to the analog/digital converter. Based on the impulse response of the circuit, a restoration filter was proposed to correct the banding, but it must be truncated for improving computational efficiency. This paper proposes an efficient and exact correction algorithm by modeling an offset voltage of a preamplifier as a first-order differential equation. It is shown that the same impulse response is derived from the differential equation. As the differential equation can keep track of the offset voltage as a state variable, the ME of the preamplifier circuit can be corrected not only effectively but also seamlessly by taking the internal calibration interval and calibration lamp status into consideration. The parameters of the circuit are estimated from nighttime unprocessed TM data of 1996, and they are used to correct daytime data of the same year. It is shown that the proposed state-space approach is more computationally efficient than the previous restoration-filter approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new algorithm for the enhancement of plastic-cased antipersonnel mine detection using a video-impulse ground-penetrating radar (GPR). The algorithm is implemented as a nonlinear signal processor, which searches for the presence of a reference waveform in a 1-D GPR echo return. The reference waveform represents a class of targets within a certain environment. The processor marks the presence of all responses similar to the reference waveform with a sharp monocycle. Simultaneously, responses with different waveforms, which presumably correspond to clutter, are suppressed. The reference waveform and other algorithm parameters are determined from training data sets acquired in a controlled environment. After training, the algorithm can be successfully applied at sites where soil, targets, and measurement scenarios are similar but not identical to those of the training site. The processor is integrated into an automated data processing and mine detection scheme as an additional clutter suppression step. The scheme consists of clutter suppression, synthetic aperture radar focusing, construction of a confidence map, and automated detection in it. The suggested algorithm is tested on experimental data, and its performance is compared against schemes where clutter suppression is organized by means of background removal and the cross correlation with a reference wavelet. The performance comparison is done in terms of receiver operating characteristic curves. It has been found that the suggested algorithm reduces the false alarm rate in about two and a half times in comparison to the cross-correlation-based clutter suppression. [ABSTRACT FROM AUTHOR]

This paper presents the use of a new distribution for fully polarimetric image classification. Several classification strategies are compared in order to assess the importance of a careful statistical modeling of the data and the complementary nature of the information provided by different frequencies. Spatial context, which is relevant in order to obtain good results with noisy data, is described by means of the multiclass Potts model, and an iterated conditional modes classification algorithm that employs pseudolikelihood is proposed. The data are described using multivariate Gaussian laws and fully multilook polarimetric distributions arising from the multiplicative model. L-band, C-band, and both bands are used to assess the influence of dimensionality on the classification. Contextual and pointwise maximum-likelihood classifications are compared using real data. Results show that both context and number of frequencies contribute for better classification products, and that, a careful statistical description of the data leads to improved results. [ABSTRACT FROM AUTHOR]

In January 2006, the Data Fusion Committee of the IEEE Geoscience and Remote Sensing Society launched a public contest for pansharpening algorithms, which aimed to identify the ones that perform best. Seven research groups worldwide participated in the contest, testing eight algorithms following different philosophies [component substitution, multiresolution analysis (MRA), detail injection, etc.]. Several complete data sets from two different sensors, namely, QuickBird and simulated Pléiades, were delivered to all participants. The fusion results were collected and evaluated, both visually and objectively. Quantitative results of pansharpening were possible owing to the availability of reference originals obtained either by simulating the data collected from the satellite sensor by means of higher resolution data from an airborne platform, in the case of the Pléiades data, or by first degrading all the available data to a coarser resolution and saving the original as the reference, in the case of the QuickBird data. The evaluation results were presented during the special session on Data Fusion at the 2006 International Geoscience and Remote Sensing Symposium in Denver, and these are discussed in further detail in this paper. Two algorithms outperform all the others, the visual analysis being confirmed by the quantitative evaluation. These two methods share the same philosophy: they basically rely on MRA and employ adaptive models for the injection of high-pass details. [ABSTRACT FROM AUTHOR]

The Moderate Resolution Imaging Spectrometer (MODIS) aboard Terra and Aqua platforms are contaminated by stripe noises. There are three types of stripe noises in MODIS data: detector-to-detector stripes, mirror side stripes, and noisy stripes. Without correction, stripe noises will cause processing errors to the other MODIS products. In this paper, a noise-reduction algorithm is developed to reduce the stripe noise effects in both Terra MODIS and Aqua MODIS data by combining a histogram- matching algorithm with an iterated weighted least-squares (WLS) facet filter. Histogram matching corrects for detector-to-detector stripes and mirror side stripes. The iterated WLS facet filter corrects for noisy stripes. The method was tested on heavily striped Terra MODIS and Aqua MODIS images. Results of Terra MODIS and Aqua MODIS data show that the proposed algorithm reduced stripes noises without degrading image quality. To evaluate performance of the proposed method, quantitative and qualitative analyses were carried out by visual inspection and quality indexes of destriped images. [ABSTRACT FROM AUTHOR]

This paper presents the Automatic Registration of Remote-Sensing Images (ARRSI); an automatic registration system built to register satellite and aerial remotely sensed images. The system is designed specifically to address the problems associated with the registration of remotely sensed images obtained at different times and/or from different sensors. The ARRSI system is capable of handling remotely sensed images geometrically distorted by various transformations such as translation, rotation, and shear. Global and local contrast issues associated with remotely sensed images are addressed in ARRSI using control-point detection and matching processes based on a phase-congruency model. Intensity-difference issues associated with multimodal registration of remotely sensed images are addressed in ARRSI through the use of features that are invariant to intensity mappings during the control-point matching process. An adaptive control-point matching scheme is employed in ARRSI to reduce the performance issues associated with the registration of large remotely sensed images. Finally, a variation on the Random Sample and Consensus algorithm called Maximum Distance Sample Consensus is introduced in ARRSI to improve the accuracy of the transformation model between two remotely sensed images while minimizing computational overhead. The ARRSI system has been tested using various satellite and aerial remotely sensed images and evaluated based on its accuracy and computational performance. The results indicate that the registration accuracy of ARRSI is comparable to that produced by a human expert and improvement over the baseline and multimodal sum of squared differences registration techniques tested. [ABSTRACT FROM AUTHOR]