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Terrestrial laser scanning is a promising tool for estimating leaf angle (including leaf inclination and azimuthal angles) distribution (LAD). However, previous studies focus on the retrieval of leaf inclination angle distribution, very few studies have considered the distribution of leaf azimuthal angle due to the restriction of measurement techniques. In this paper, we developed a new method to obtain more accurate leaf inclination and azimuthal angle estimations based on leaf point cloud segmentation and filtration and then fit LAD functions using two-parameter Beta-distribution model. In addition, we constructed a new projection coefficient model with two parameters $G(\theta $ , $\varphi$) using Nilson’s algorithm based on the accurate retrieval of LAD. To assess the influence of leaf numbers on leaf inclination and azimuthal angle estimations, we modeled 160 individual leaves and 10 trees with different leaf numbers. In addition, to validate the final results, we also sampled three magnolia trees with different leaf numbers and manually measured leaf inclination and azimuthal angles of all their leaves using an angle measurement device. All results showed that the method proposed in this paper can provide accurate leaf inclination and azimuthal angles (leaf inclination angle: $R^{2} = 0.98$ , RMSE = 24° and leaf azimuthal angle: $R^{2} = 0.99$ , RMSE = 3.44°). The simulated LAD and $G(\theta $ , $\varphi$) estimations based on these leaf inclination and azimuthal angles were strongly correlated with those obtained from ground truth measurements ($P >0.05$). [ABSTRACT FROM AUTHOR]

The concept of information fusion has gained a widespread interest in many fields due to its complementary properties. It makes systems more robust against uncertainty. This paper presents a new approach for the well-logging estimation problem by using a fusion methodology. The natural gammaray tool (NGT) is considered as an important instrument in the well logging. The NGT detects changes in natural radioactivity emerging from the variations in concentrations of micronutrients as uranium (U), thorium (Th), and potassium (K). The main goal of this paper is to have precise estimation of the concentrations of U, Th, and K. Four types of Kalman filters are designed to estimate the elements using the NGT sensor. Then, a fusion of the Kalman filters is utilized into an integrated framework by an ordered weighted averaging (OWA) operator to enhance the quality of the estimations. A real covariance of the output error based on the innovation matrix is utilized to design weighting factors for the OWA operator. The simulation studies indicate not only a reliable performance of the proposed method compared with the individual Kalman filters but also a better response in contrast with previous fusion methodologies. [ABSTRACT FROM AUTHOR]

In this paper, we consider signal subspace estimation based on low-rank representation for hyperspectral imagery. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral remote sensing data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces in hyperspectral imagery. In particular, we develop simple estimation approaches without user-defined parameters because these parameters can be fixed as constants. Both real data experiments and computer simulations demonstrate excellent performance of the proposed approaches over those currently in the literature. [ABSTRACT FROM PUBLISHER]

This paper introduces a weighted total least squares (WTLS)-based estimator into image registration to deal with the coordinates of control points (CPs) that are of unequal accuracy. The performance of the estimator is investigated by means of simulation experiments using different coordinate errors. Comparisons with ordinary least squares (LS), total LS (TLS), scaled TLS, and weighted LS estimators are made. A novel adaptive weight determination scheme is applied to experiments with remotely sensed images. These illustrate the practicability and effectiveness of the proposed registration method by collecting CPs with different-sized errors from multiple reference images with different spatial resolutions. This paper concludes that the WTLS-based iteratively reweighted TLS method achieves a more robust estimation of model parameters and higher registration accuracy if heteroscedastic errors occur in both the coordinates of reference CPs and target CPs. [ABSTRACT FROM PUBLISHER]

In this paper, we contribute to answer the question: How accurately can we estimate heights of buildings with flat roofs given one high-resolution single-pass interferometric synthetic aperture radar (InSAR) image pair and one aerial orthophoto? What makes this problem challenging are the different sensor geometries and the sound stochastic combination of all available elevation cues. We revisit already existing methods and develop novel approaches to determine building heights. A rigorous stochastic approach based on least squares adjustment with functionally dependent parameters is introduced to combine all height measurements per building to one robust height estimate. Observation accuracies of the stochastic model are either taken from the literature or estimated empirically. A major benefit of adjustment is that it delivers a posterior standard deviation per height, which can be interpreted as a precision indicator and is of high relevance for practical applications. Estimated heights of an urban scene are compared to ground truth acquired with airborne laser scanning, allowing us to assess height accuracies that can be achieved under nearly optimal conditions. We conduct statistical tests that validate our model and show that a weighted combination of optical and synthetic aperture radar (SAR) data with least squares adjustment delivers reliable height estimates with meter accuracy for flat-roofed buildings. Additionally, we empirically estimate a confidence interval of the estimated heights that directly tells the user the security margin to be included, for example, in case of building evacuations for an anticipated flooding event, under the condition that the data and model have the same specifications as in this paper. [ABSTRACT FROM PUBLISHER]

This paper derives the Cramér–Rao lower bound (CRLB) for a general multichannel spaceborne synthetic aperture radar system for ground moving-target indication and provides a theoretical analysis of across-track velocity estimation for RADARSAT-2 and a fictitious satellite. The signal model assumes a deterministic target signal in Gaussian clutter and noise. In estimation theory, the CRLB provides a lower bound on the achievable variance of any unbiased estimator. An estimator that achieves this bound is called efficient; however, there is no guarantee that an efficient estimator can be found. Nonetheless, the theoretical variance of the efficient estimator provides a good measure of the capability of the system and serves as a valuable system performance validation tool. Even if an efficient estimator cannot be found, for radar systems, the CRLB provides a necessary, but not sufficient design baseline for measurement parameters, such as the number of subapertures for transmit and receive, power levels, pulse-repetition frequency, and so on. CRLBs for a limited number of system configurations for RADARSAT-2 and TerraSAR-X are presented by Cerutti-Maori et al. and Ender et al.; however, the complete analytical form of CRLB for a general system, which can be readily applied to an arbitrary antenna switching configuration, has not been previously derived. [ABSTRACT FROM PUBLISHER]

The objective of synthetic aperture radar (SAR) edge detection is the identification of contours across the investigated scene, exploiting SAR complex data. Edge detectors available in the literature exploit singularly amplitude and interferometric phase information, looking for reflectivity or height difference between neighboring pixels, respectively. Recently, more performing detectors based on the joint processing of amplitude and interferometric phase data have been presented. In this paper, we propose a novel approach based on the exploitation of real and imaginary parts of single-look complex acquired data. The technique is developed in the framework of stochastic estimation theory, exploiting Markov random fields. Compared to available edge detectors, the technique proposed in this paper shows useful advantages in terms of model complexity, phase artifact robustness, and scenario applicability. Experimental results on both simulated and real TerraSAR-X and COSMO-SkyMed data show the interesting performances and the overall effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, a new approach based on two fusion schemes is proposed to overcome the uncertainties in land surface emissivity (LSE) estimation and, consequently, land surface temperature (LST) retrieval. The fusion schemes are called image-based weighted methods and knowledge-based weighted methods, in which each of them includes two LSE estimation methods. The effectiveness of the two proposed fusion schemes is empirically tested over two scenes of Landsat-8 (known as Landsat Data Continuity Mission) data sets, and the obtained LSEs by individual and proposed methods were compared to the LSE product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) by image-based and class-based cross-comparison. In both scenes, the adjusted normalized emissivity method (ANEM) and NDVI-based emissivity method (NBEM) provide appropriate results among five individual methods. In contrast, weighted to median (WMED) achieves superior results among the proposed fusion methods for both scenes. In addition, the root-mean-square error (rmse) values of LSE obtained by ANEM and WMED are 1.48% and 0.87%, which lead to 1.25 K and 0.73 K errors in the LST retrieval by the single-channel algorithm in the first scene, respectively. For the second scene, the error values of NBEM and WMED are 1.10% and 0.52%, which lead to 0.93 K and 0.44 K errors in the LST, respectively. Moreover, the error ranges and rmse of cross-comparison for the obtained LSE in the proposed methods were remarkably decreased. Also, in this research, for LST cross-comparison, an alternative scaling method based on LST products of the Moderate Resolution Imaging Spectroradiometer was proposed. The LST validation results demonstrated that the proposed methods provide better estimates in terms of three accuracy measures in both examined data sets. [ABSTRACT FROM AUTHOR]

Polarimetric incoherent target decomposition aims at accessing physical parameters of illuminated scatters through the analysis of the target coherence or covariance matrix. In this framework, independent component analysis (ICA) was recently proposed as an alternative method to eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high-resolution synthetic aperture radar systems). Until now, the two main drawbacks reported of the aforementioned method are the greater number of samples required for an unbiased estimation, when compared to the classical eigenvector decomposition, and the inability to be employed in scenarios under the Gaussian clutter assumption. In this paper, both drawbacks are analyzed. First, a Monte Carlo approach is performed in order to investigate the bias in estimating Touzi's target-scattering-vector-model parameters when ICA is employed. Simulated data and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the bias estimation under different scenarios. Finally, the performance of the algorithm is also evaluated under the Gaussian clutter assumption and when spatial correlation is introduced in the model. [ABSTRACT FROM AUTHOR]

This paper presents an automated algorithm for rapidly and effectively detecting cars directly from large-volume 3-D point clouds. Rather than using low-order descriptors, a multilayer feature generation model is created to obtain high-order feature representations for 3-D local patches through deep learning techniques. To handle cars with different levels of incompleteness caused by data acquisition ways and occlusions, a hierarchical visibility estimation model is developed to augment Hough voting. Considering scale and orientation variations in the azimuth direction, a set of multiscale Hough forests is constructed to rotationally cast votes to estimate cars' centroids. Quantitative assessments show that the proposed algorithm achieves average completeness, correctness, quality, and F1-measure of 0.94, 0.96, 0.90, and 0.95, respectively, in detecting 3-D cars. Comparative studies also demonstrate that the proposed algorithm outperforms the other four existing algorithms in accurately and completely detecting 3-D cars from large-scale 3-D point clouds. [ABSTRACT FROM AUTHOR]

This paper proposes a new Bayesian strategy for the smooth estimation of altimetric parameters. The altimetric signal is assumed to be corrupted by a thermal and speckle noise distributed according to an independent and non-identically Gaussian distribution. We introduce a prior enforcing a smooth temporal evolution of the altimetric parameters which improves their physical interpretation. The posterior distribution of the resulting model is optimized using a gradient descent algorithm which allows us to compute the maximum a posteriori estimator of the unknown model parameters. This algorithm has a low computational cost that is suitable for real-time applications. The proposed Bayesian strategy and the corresponding estimation algorithm are evaluated using both synthetic and real data associated with conventional and delay/Doppler altimetry. The analysis of real Jason-2 and CryoSat-2 waveforms shows an improvement in parameter estimation when compared to state-of-the-art estimation algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, a novel spectral–spatial adaptive sparse representation (SSASR) method is proposed for hyperspectral image (HSI) denoising. The proposed SSASR method aims at improving noise-free estimation for noisy HSI by making full use of highly correlated spectral information and highly similar spatial information via sparse representation, which consists of the following three steps. First, according to spectral correlation across bands, the HSI is partitioned into several nonoverlapping band subsets. Each band subset contains multiple continuous bands with highly similar spectral characteristics. Then, within each band subset, shape-adaptive local regions consisting of spatially similar pixels are searched in spatial domain. This way, spectral–spatial similar pixels can be grouped. Finally, the highly correlated and similar spectral–spatial information in each group is effectively used via the joint sparse coding, in order to generate better noise-free estimation. The proposed SSASR method is evaluated by different objective metrics in both real and simulated experiments. The numerical and visual comparison results demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Real-time radial velocity estimation is a key challenge for moving target imaging and location in current single-antenna synthetic aperture radar (SAR)-ground moving target indication systems. Since the conventional methods suffer from ambiguity, complexity realization, or heavy computation load for fast moving target motion estimation, this paper emphasizes the estimation efficiency by simple realization. An efficient Radon transform (RT) estimation is proposed to estimate the radial velocity of fast moving target by utilizing the geometry information, and much more geometry information is exploited to realize clutter cancellation, noise cancellation, and estimation error minimizing in the RT domain, which is not proposed by the others. With only two to four angles used to calculate rather than search for the radial velocity of moving targets, the proposed methods simplify the conventional range and angle (2-D) searching procedure into several time range (1-D) searching procedure efficiently. The theoretical and experimental analysis provides qualitative and quantitative evaluations into the effectiveness of the proposed methods. In the single-antenna SAR system, the proposed methods can estimate the radial velocity of fast moving target efficiently and accurately in high signal to clutter plus noise ratio scenarios. [ABSTRACT FROM PUBLISHER]

Anomaly detection has been an important topic in hyperspectral image analysis. This technique is sometimes more preferable than the supervised target detection because it requires no a priori information for the interested materials. Many efforts have been made in this topic; however, they usually suffer from excessive time cost and a high false-positive rate. There are two major problems that lead to such a predicament. First, the construction of the background model and affinity estimation are often overcomplicated. Second, most of these methods have to impose a stringent assumption on the spectrum distribution of background; however, these assumptions cannot hold for all practical situations. Based on this consideration, this paper proposes a novel method allowing for fast yet accurate pixel-level hyperspectral anomaly detection. We claim the following main contributions: construct a high-order 2-D crossing approach to find the regions of rapid change in the spectrum, which runs without any a priori assumption; and design a low-complexity discrimination framework for fast hyperspectral anomaly detection, which can be implemented by a series of filtering operators with linear time cost. Experiments on three different hyperspectral images containing several pixel-level anomalies demonstrate the superiority of the proposed detector compared with the benchmark methods. [ABSTRACT FROM PUBLISHER]

A time or spatial series of drop counts is but one realization of a multiple stochastic process. In this paper, a method is presented that extracts more of the information contained in the time series of 1-min Joss-Waldvogel disdrometer counts in rain than a simple analysis of the magnitudes of the counts would provide. This is done by greatly increasing the size of a data set using a Bayesian analysis of drop count measurements in 17 size bins. Using the empirical copula statistical technique of probability density function transformations, a 1391-min time series of drop counts was expanded to the equivalent of 40 000 min. This dramatic increase in sample size permits a deeper characterization of the rain. Using this single disdrometer, it also allows one to translate these counts into a 200 × 200 grid filled at each point with drop size distributions of mean drop concentrations consistent with the observed statistical properties of the rain. Such a field can be used for remote sensing studies of the effect of partial beam filling and for algorithm development. Moreover, since there is nothing unique to this set of drop counts, this approach can be applied to any other set of count data, including snow and clouds. [ABSTRACT FROM PUBLISHER]

This paper presents a denoising approach for multitemporal synthetic aperture radar (SAR) images based on the concept of nonlocal means (NLM). It exploits the information redundancy existing in multitemporal images by a two-step strategy. The first step realizes a nonlocal weighted estimation driven by the redundancy in time, whereas the second step makes use of the nonlocal estimation in space. Using patch similarity miss-registration estimation, we also adapted this approach to the case of unregistered SAR images. The experiments illustrate the efficiency of the proposed method to denoise multitemporal images while preserving new information. [ABSTRACT FROM PUBLISHER]

For multichannel synthetic aperture radar based ground moving-target indication (SAR-GMTI), the effective baseline usually needs to be estimated in order to obtain an accurate estimate of radial velocity for each moving target. This paper deals with the effects of Doppler aliasing on baseline estimation. We show that Doppler aliasing can introduce an interferometric phase uncertainty as well as an interferometric phase bias. The phase uncertainty will increase the variance of the baseline estimate, whereas the phase bias will bias the baseline estimate. To address the aforementioned effects caused by Doppler aliasing, a new method for estimating the effective baseline is proposed. One of the key steps of this method is to perform an operation called sample censoring aimed at mitigating the problem of estimation bias. The censoring threshold can be approximately determined by introducing a concept referred to as equivalent variance for the interferometric phases of the entire Doppler bins. Moreover, in order to account for the variation of interferometric phase variance over different Doppler bins, a strategy of weighting is adopted. Experimental results from SAR-GMTI data validate the effectiveness of the newly proposed baseline estimation method. [ABSTRACT FROM PUBLISHER]

This paper presents a new automatic change detection technique for synthetic aperture radar (SAR) time series, i.e., Method for generalIzed Means Ordered Series Analysis (MIMOSA). The method compares only two different temporal means between the amplitude images, whatever the length of the time series. The method involves three different steps: 1) estimation of the amplitude distribution parameters over the images; 2) computation of the theoretical joint probability density function between the two temporal means; and 3) automatic thresholding according to a given false alarm rate, which is the only change detection parameter. The procedure is executed with a very low computational cost and does not require any spatial speckle filtering. Indeed, the full image resolution is used. Due to the temporal means, the data volume to process is reduced, which is very helpful. Moreover, the two means can be simply updated using the new incoming images only. Thus, the full time series is not processed again. Change detection results between image pairs are presented with the airborne sensor CARABAS-II, using a public data release, and with TerraSAR-X data. In the case of time series, change detection results are illustrated using a TerraSAR-X time series. In every case, the MIMOSA method produces very good results. [ABSTRACT FROM PUBLISHER]

Sparsity-based unmixing algorithms, exploiting the sparseness property of the abundances, have recently been proposed with promising performances. However, these algorithms are developed for the linear mixture model (LMM), which cannot effectively handle the nonlinear effects. In this paper, we extend the current sparse regression methods for the LMM to bilinear mixture models (BMMs), where the BMMs introduce additional bilinear terms in the LMM in order to model second-order photon scattering effects. To solve the abundance estimation problem for the BMMs, we propose to perform a sparsity-based abundance estimation by using two dictionaries: a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible second-order endmember interaction components. Then, the abundance values can be estimated from the sparse codes associated with the linear dictionary. Moreover, to exploit the spatial data structure where the adjacent pixels are usually homogeneous and are often mixtures of the same materials, we first employ the joint-sparsity (row-sparsity) model to enforce structured sparsity on the abundance coefficients. However, the joint-sparsity model is often a strict assumption, which might cause some aliasing artifacts for the pixels that lie on the boundaries of different materials. To deal with this problem, the low-rank-representation model, which seeks the lowest rank representation of the data, is further introduced to better capture the spatial data structure. Our simulation results demonstrate that the proposed algorithms provide much enhanced performance compared with state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]