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In this paper, we present a method of ship size extraction for Sentinel-1 synthetic aperture radar (SAR) images, which is composed of the image processing stage and the regression stage. In order to achieve extraction with high accuracy, considering the data characteristics of Sentinel-1 images, we propose to use the dual-polarization fusion and the nonlinear regression with the gradient boosting. The experiments and analyses on a relatively large data set show that: 1) compared with the existing and related studies, the proposed method achieves an improved performance. The extraction errors are pushed under one pixel, and they are 4.66% (8.80 m) and 7.01% (2.17 m) for length and width, respectively; 2) the dual-polarization information fusion does improve the size extraction accuracy; and 3) the nonlinear regression does exploit the relationship between the influential factors and the size parameters and provide a better performance than the linear regression. The experimental results verify that the proposed design is suitable for ship size extraction in Sentinel-1 SAR images. [ABSTRACT FROM AUTHOR]

The aim of this paper was to test the capabilities of the Sentinel-1 radar data in downscaling Soil Moisture Active Passive (SMAP) radiometer data for high-resolution soil moisture estimation. Three different active–passive downscaling algorithms, including the brightness temperature-based downscaling algorithm (BTBDA), the soil moisture-based downscaling algorithm (SMBDA), and a change detection method (CDM), were analyzed using pairs of Sentinel-1 active and SMAP passive observations collected over a semiarid landscape in southeastern Australia from May 2015 to May 2016. While these algorithms have been tested previously, this is the first study to evaluate the three algorithms using real Sentinel-1 radar and SMAP radiometer data. The SMAP passive observations were disaggregated to 9-, 3-, and 1-km scales and then compared with ground soil moisture measurements. The results suggest that the root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.057, 0.056, and 0.067 cm3/cm3 for the BTBDA, SMBDA, and CDM, respectively. The accuracy of downscaling methods was generally decreased when applied at the finer spatial resolution. The SMBDA had overall better performance in terms of correctly detecting the soil moisture pattern and relatively lower RMSE values, and is, therefore, recommended for the combined Sentinel-1 radar and SMAP radiometer setup for soil moisture monitoring. The influence of incidence angle normalization of Sentinel-1 SAR data on downscaled soil moisture was also investigated and found to be minimal. [ABSTRACT FROM AUTHOR]

Dimensionality reduction is an essential task in hyperspectral image processing. How to preserve the original intrinsic structure information and enhance the discriminant ability is still a challenge in this area. Recently, with the advantage of preserving global intrinsic structure information, low-rank representation has been applied to dimensionality reduction and achieved promising performance. By exploiting the submanifold information of the original data set, multimanifold learning is effective in enhancing the discriminant ability of the processed data set. In addition, due to the ability of preserving the spatial neighborhood structure information, the tensor analysis has become a popular technique for hyperspectral image processing. Motivated by the above-mentioned analysis, a novel tensor-based low-rank graph with multimanifold regularization (T-LGMR) for dimensionality reduction of hyperspectral images is proposed in this paper. In the T-LGMR, a low-rank constraint is employed to preserve the global data structure while multimanifold information is utilized to enhance the discriminant ability, and tensor representation is used to preserve the spatial neighborhood information. Finally, dimensionality reduction is achieved in the graph embedding framework. Experimental results on three real hyperspectral data sets demonstrate the superiority of the proposed method over several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Flood inundation probability is critical for situation awareness, flood mitigation, emergency response, and postevent damage assessment. Current flood inundation mapping approaches can be categorized into real-time (RT) and near-RT (NRT) processes based on the timing of data acquisition. However, the intrinsic limitations of each category largely hamper their applications for flood mapping. Taking the 2015 South Carolina flood in downtown Columbia as a case study, this paper proposes a flood inundation reconstruction model by enhancing the NRT normalized difference water index (NDWI) derived from remote sensing imagery with the RT data including stream gauge readings and social media (tweets). Splitting into three modules: water height module, global enhancement module, and local enhancement module, the proposed model first incorporates the gauge readings and the NDWI image to reconstruct a macroscale flood probability layer, which is then locally enhanced using the verified flood-related tweets. The final output of the model matches well with the U.S. Geological Survey inundation map and its surveyed high-water marks. Results suggest that by enhancing NRT imagery with RT data sources, the proposed flood inundation probability reconstruction model renders a more robust, spatially enhanced flood probability index for emergency responders to quickly identify areas in need of urgent attention. [ABSTRACT FROM AUTHOR]

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

Recent advances in computer vision and pattern recognition have demonstrated the superiority of deep neural networks using spatial feature representation, such as convolutional neural networks (CNNs), for image classification. However, any classifier, regardless of its model structure (deep or shallow), involves prediction uncertainty when classifying spatially and spectrally complicated very fine spatial resolution (VFSR) imagery. We propose here to characterize the uncertainty distribution of CNN classification and integrate it into a regional decision fusion to increase classification accuracy. Specifically, a variable precision rough set (VPRS) model is proposed to quantify the uncertainty within CNN classifications of VFSR imagery and partition this uncertainty into positive regions (correct classifications) and nonpositive regions (uncertain or incorrect classifications). Those “more correct” areas were trusted by the CNN, whereas the uncertain areas were rectified by a multilayer perceptron (MLP)-based Markov random field (MLP-MRF) classifier to provide crisp and accurate boundary delineation. The proposed MRF-CNN fusion decision strategy exploited the complementary characteristics of the two classifiers based on VPRS uncertainty description and classification integration. The effectiveness of the MRF-CNN method was tested in both urban and rural areas of southern England as well as semantic labeling data sets. The MRF-CNN consistently outperformed the benchmark MLP, support vector machine, MLP-MRF, CNN, and the baseline methods. This paper provides a regional decision fusion framework within which to gain the advantages of model-based CNN, while overcoming the problem of losing effective resolution and uncertain prediction at object boundaries, which is especially pertinent for complex VFSR image classification. [ABSTRACT FROM AUTHOR]

Ocean color data are critical for the monitoring and understanding of biological and ecological processes and phenomena, and the data are also important sources of input data for physical and biogeochemical ocean models. The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership has continued to provide global ocean color data since its launch in October 2011. However, there are always many missing pixels in the original VIIRS-measured ocean color images due to clouds and various other reasons. The data interpolating empirical orthogonal functions (DINEOF) is a method to reconstruct (gap filling) missing data in geophysical data sets based on the empirical orthogonal function. In this paper, the DINEOF is applied to VIIRS-derived global Level-3 binned ocean color data of 9-km spatial resolution, and the DINEOF-reconstructed ocean color data are used to fill the gaps of missing data. In particular, daily, eight-day, and monthly VIIRS global Level-3 binned ocean color data, including chlorophyll-a concentration, diffuse attenuation coefficient at 490 nm [ $K_{d}(490)$ ], and normalized water-leaving radiance spectra [ $nL_{w}(\lambda)$ ] at the five VIIRS visible bands, are tested and evaluated. To validate and evaluate the gap-filled data, a set of original valid nonmissing pixels in the VIIRS images are selected randomly and treated as missing pixels in the DINEOF process, so that the reconstructed pixels can be compared with the original data. Results show that the DINEOF method can successfully reconstruct and gap-fill meso-scale and large-scale spatial ocean features in the global VIIRS Level-3 images, as well as capture the temporal variations of these features. [ABSTRACT FROM AUTHOR]

In this paper, a dual-frequency and dual-baseline (DFDB) processing framework for the extraction of high-precision terrain information from airborne interferometric synthetic aperture radar (SAR) data is presented. Specifically, we propose the use of two single-pass data sets acquired simultaneously in two different frequency bands and two large-baseline repeat-pass data sets also acquired simultaneously in two frequency bands. The configuration profits from the stability of the single-pass derived elevation maps in relation to spatially correlated artifacts as well as from the increased sensitivity associated with large-baseline acquisitions. Moreover, the dual-frequency nature of the data set enables the tackling of the phase unwrapping issue, promoting the retrieval of unambiguous measurements. Several algorithms for the interferometric processing of the DFDB airborne data set are proposed, including the outline of multichannel phase calibration and unwrapping error correction strategies and approaches to remove spatially correlated artifacts and extract the common underlying topography. Elevation models generated from a DFDB data set acquired with the airborne F-SAR sensor over tidal flats in northern Germany are presented, and comparisons with an airborne laser scanner reference show errors with a standard deviation of around 14 cm and a mean absolute deviation of less than 10 cm. [ABSTRACT FROM AUTHOR]

Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced. In this paper, a novel method of missing information reconstruction in remote sensing images is proposed. The unified spatial–temporal–spectral framework based on a deep convolutional neural network (CNN) employs a unified deep CNN combined with spatial–temporal–spectral supplementary information. In addition, to address the fact that most methods can only deal with a single missing information reconstruction task, the proposed approach can solve three typical missing information reconstruction tasks: 1) dead lines in Aqua Moderate Resolution Imaging Spectroradiometer band 6; 2) the Landsat Enhanced Thematic Mapper Plus scan line corrector-off problem; and 3) thick cloud removal. It should be noted that the proposed model can use multisource data (spatial, spectral, and temporal) as the input of the unified framework. The results of both simulated and real-data experiments demonstrate that the proposed model exhibits high effectiveness in the three missing information reconstruction tasks listed above. [ABSTRACT FROM AUTHOR]

We have introduced a new decomposition method for seismic data, termed complex variational mode decomposition (VMD), and we have also designed a new filtering technique for random noise attenuation in seismic data by applying the VMD on constant-frequency slices in the frequency-offset (f-x) domain. The motivation behind this paper is to overcome the potential low performance of empirical mode decomposition (EMD) for energy preservation of the steeply dipping events when used for noise attenuation, and low resolution when used for signal decomposition. The VMD is proposed to decompose a signal into an ensemble of band-limited modes. For seismic data consisting of linear events, the constant-frequency slices of its f-x spectrum are exactly band-limited. The noise attenuation algorithm is summarized as follows. First, the Fourier transform is applied on the time axis of the 2-D seismic data. Next, the VMD is applied on each frequency slice of the f-x spectrum and the decomposed modes are combined to obtain the filtered frequency slice. Finally, an inverse Fourier transform is applied on the frequency axis of the f-x spectrum to obtain the denoised result. The resulting VMD-based noise attenuation method is equivalent to applying a Wiener filter on each decomposed mode, which is achieved during the decomposition progress. We also applied 2-D VMD on 3-D seismic data for denoising. Numerical results show that the proposed VMD-based method achieves a higher denoising quality than both the f-x deconvolution method and the EMD-based denoising method, especially for preserving the steep slopes. [ABSTRACT FROM AUTHOR]

An efficient 2-D joint full-waveform inversion method for electromagnetic and seismic data in a layered medium background is developed. The joint inversion method based on the integral equation (IE) method is first proposed in this paper. In forward computation, the IE method is employed, which usually has smaller discretized computation domain and less cumulative error compared with the finite-difference method. In addition, fast Fourier transform is used to accelerate the convolution between Green's functions and induced sources due to the shift invariance property of the layered Green's functions in the horizontal direction. In the inversion model, the cross-gradient function is incorporated into the cost function of the separate inversion to enforce the structure similarity between electric conductivity and seismic-wave velocity. We use the improved variational Born iteration method and two different iteration strategies to minimize the cost function and reconstruct the contrasts. Several typical models in geophysical applications are used to validate our joint inversion method, and the numerical simulation results show that joint inversion can improve the inversion results when compared with those from the separate inversion. [ABSTRACT FROM AUTHOR]

The supervised deep networks have shown great potential in improving the classification performance. However, training these supervised deep networks is very challenging for hyperspectral image given the fact that usually only a small amount of labeled samples are available. In order to overcome this problem and enhance the discriminative ability of the network, in this paper, we propose a deep network architecture for a super-resolution (SR)-aided hyperspectral image classification with classwise loss (SRCL). First, a three-layer SR convolutional neural network (SRCNN) is employed to reconstruct a high-resolution image from a low-resolution image. Second, an unsupervised triplet-pipeline CNN (TCNN) with an improved classwise loss is built to encourage intraclass similarity and interclass dissimilarity. Finally, SRCNN, TCNN, and a classification module are integrated to define the SRCL, which can be fine-tuned in an end-to-end manner with a small amount of training data. Experimental results on real hyperspectral images demonstrate that the proposed SRCL approach outperforms other state-of-the-art classification methods, especially for the task in which only a small amount of training data are available. [ABSTRACT FROM AUTHOR]

The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. Most works in deep learning have achieved a great success on regular input representations, but they are hard to be directly applied to classify point clouds due to the irregularity and inhomogeneity of the data. In this paper, a deep neural network with spatial pooling (DNNSP) is proposed to classify large-scale point clouds without rasterization. The DNNSP first obtains the point-based feature descriptors of all points in each point cluster. The distance minimum spanning tree-based pooling is then applied in the point feature representation to describe the spatial information among the points in the point clusters. The max pooling is next employed to aggregate the point-based features into the cluster-based features. To assure the DNNSP is invariant to the point permutation and sizes of the point clusters, the point-based feature representation is determined by the multilayer perception (MLP) and the weight sharing for each point is retained, which means that the weight of each point in the same layer is the same. In this way, the DNNSP can learn the features of points scaled from the entire regions to the centers of the point clusters, which makes the point cluster-based feature representations robust and discriminative. Finally, the cluster-based features are input to another MLP for point cloud classification. We have evaluated qualitatively and quantitatively the proposed method using several airborne laser scanning and terrestrial laser scanning point cloud data sets. The experimental results have demonstrated the effectiveness of our method in improving classification accuracy. [ABSTRACT FROM AUTHOR]

This paper addresses an autofocusing technique for highly squinted airborne synthetic aperture radar (SAR) imaging. In highly squinted mode, the phase errors resulting by trajectory deviations usually exhibit both range and cross-range variance. To circumvent this problem, a 2-D space-variant phase error model is proposed. Then an autofocusing approach is presented to realize precise SAR reconstruction. This approach mainly consists of three processing steps. First, multiple local images are automatically selected based on sliding window operation in both range and cross-range dimensions. For each local image, weighted squint phase gradient autofocus kernel is applied to estimate the local phase error function. Second, the derived multiple local phase error functions are combined to resolve the 2-D space-variant phase error model using a weighted total least square method. Third, the fast factorized back-projection algorithm with pixelwise phase error correction is utilized to obtain focused image eventually. Experiments on both simulated and real-measured SAR data sets validate the focusing performance of the proposed approach. [ABSTRACT FROM AUTHOR]

The mapping of urban areas at regional to global scales is a crucial task due to its value for environmental monitoring, habitat and biodiversity conservation, and decision-making. In most current applications, two techniques (i.e., supervised classification and data fusion) are widely applied in large-scale urban mapping. However, the costly training sample collection, inadequate data-source descriptions, and diverse urban characteristics (e.g., shape, size, socioeconomic status, and physical environment) are challenging problems for the urban mapping approaches. In this context, aiming at effectively deriving accurate urban areas at a large scale, we propose a novel ensemble support vector machine (SVM) method which consists of three steps: 1) the automatic generation of training data to reduce labor costs; 2) the construction of an ensemble SVM model to effectively combine the multisource data (including remote sensing and socioeconomic data); and 3) an adaptive patch-based thresholding technique to tackle the diverse urban characteristics. The proposed method is employed to map urban areas of China in 2005 and 2010, and the resulting maps are compared with the existing urban maps for 287 prefecture-level cities. It is found that our results present a satisfactory superiority, especially in challenging small cities, with a significant improvement in median Kappa (0.174 for 2005 and 0.203 for 2010). When incorporating moderate-resolution imaging spectroradiometer multispectral data as an additional source, the Kappa coefficient can be further raised by 0.028 for 2010. In general, the proposed method shows great potential for accurately mapping urban areas at regional, continental, or even global scales in a cost-effective manner. [ABSTRACT FROM AUTHOR]

Merging microwave radiances and modeled estimates of snowpack states in a data assimilation scheme is a potential method for snowpack characterization. A radiance assimilation scheme for snow requires a land surface model (LSM) coupled to a radiative transfer model (RTM). In this paper, we explore the degree of model fidelity required in order for radiance assimilation to yield benefits for snowpack characterization. Specifically, we characterize the uncertainty of Microwave Emission Model for Layered Snowpacks (MEMLS) radiance predictions by quantifying model accuracy and sensitivity to the following: 1) the LSM snowpack layering scheme and 2) the properties of the snow layers, including melt-refreeze ice layers. MEMLS was consistent with the measured brightness temperatures at 18.7 and 36.5 GHz with a bias (mean absolute error) of -0.1 K (3.1 K) for the vertical polarization and 3.4 K (9.3 K) for the horizontal polarization. An error in the predictions at horizontal polarization is due to uncertainty in ice-layer properties. It was found that in order for predicted brightness temperatures from the coupled LSM and RTM to be adequate for radiance assimilation purposes, the following must be satisfied: 1) the LSM snowpack layering scheme must accurately represent the stratigraphic snowpack layers; 2) dynamics of melt-refreeze ice layers must be modeled explicitly, and the predicted density of melt-refreeze layers must be accurate within ±40 kg · m-3; and 3) the MEMLS correlation length must be predicted within ±0.016 mm, or effective optical grain diameter must be predicted within ±0.045 mm. Recommendations for future field measurements are made. [ABSTRACT FROM AUTHOR]

Parametric and nonparametric approaches to evaluate land-cover change detection using very high resolution (VHR) satellite imagery are applied to the analysis of the demolition of the Rocky Flats nuclear weapons facility located near Denver, CO. Both maximum-likelihood and neural network classifiers are used to validate a new parallel architecture which improves the accuracy when applied to VHR satellite imagery for the study of land-cover change between sequential satellite acquisitions. An enhancement of about 14% was found between the single-step classification and the new parallel architecture, confirming the advantage and the robust improvement obtained with this architecture regardless of the classification algorithm used. In this paper, we demonstrate and document the demolition and removal of hundreds of buildings taken down to bare soil between 2003 and 2005 at the Rocky Flats site. [ABSTRACT FROM AUTHOR]

This paper describes the validation of ocean surface winds derived from Oceansat-2 Scatterometer (OSCAT) using global moored buoy observations and data from Advanced Scatterometer (ASCAT) and QuikSCAT (QSCAT) scatterometer. The analysis performed with the global in situ buoy data for the period of 9 months shows the root-mean-square error of around 1.5 m/s for the wind speed and around 20 ^\circ in the direction. The OSCAT and ASCAT data comparison also shows a high correlation with the difference of 1.5 m/s for the full range of winds and \sim1.2 m/s for the range 4–24 m/s. [ABSTRACT FROM PUBLISHER]

Hyperspectral unmixing is a process to identify the constituent materials and estimate the corresponding fractions from the mixture. During the last few years, nonnegative matrix factorization (NMF), as a suitable candidate for the linear spectral mixture model, has been applied to unmix hyperspectral data. Unfortunately, the local minima caused by the nonconvexity of the objective function makes the solution nonunique, thus only the nonnegativity constraint is not sufficient enough to lead to a well-defined problem. Therefore, in this paper, two inherent characteristics of hyperspectral data, piecewise smoothness (both temporal and spatial) of spectral data and sparseness of abundance fraction of every material, are introduced to NMF. The adaptive potential function from discontinuity adaptive Markov random field model is used to describe the smoothness constraint while preserving discontinuities in spectral data. At the same time, two NMF algorithms, nonsmooth NMF and NMF with sparseness constraint, are used to quantify the degree of sparseness of material abundances. A gradient-based optimization algorithm is presented, and the monotonic convergence of the algorithm is proved. Three important facts are exploited in our method: First, both the spectra and abundances are nonnegative; second, the variation of the material spectra and abundance images is piece-wise smooth in wavelength and spatial spaces, respectively; third, the abundance distribution of each material is almost sparse in the scene. Experiments using synthetic and real data demonstrate that the proposed algorithm provides an effective unsupervised technique for hyperspectral unmixing. [ABSTRACT FROM AUTHOR]

Currently, most optical Earth observation satellites carry both a panchromatic sensor and a set of lower spatial resolution multispectral sensors. In order to benefit from both sources of information, several pansharpening methods have been developed to produce a multispectral image at the spatial resolution of the panchromatic band. The aim of this paper is to suggest a novel approach to the pansharpening problem within a Bayesian framework. This Bayesian data fusion (BDF) method relies on statistical relationships between the various spectral bands and the panchromatic band without suffering from restricting modeling hypotheses. Furthermore, it allows the user to weight the spectral and panchromatic information with respect to either visual or quantitative criteria, which leads to adaptable results according to users' needs and study areas. The performance of this approach was compared to existing methods based on markedly different subset images from very high spatial resolution IKONOS images. Results showed that BDF yielded the highest spectral consistency; Furthermore, small details were adequately added to the pansharpened images with little artifact as compared to those created using wavelet-based methods. Finally, the method was fast and easy to implement owing to its straightforward formulation. As it does not have any intrinsic limitations on the type of data to be processed or the number of bands to be merged, it also appears to be very promising for optical/SAR or hyperspectral image fusion. [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, a hybrid reconstruction algorithm (HRA) is presented to solve the ill-posed inverse problem associated with 3-D ionospheric stochastic tomography. In this new method, the ionospheric electron density (IED) can be inverted by using two steps. First, a truncated singular value decomposition (TSVD) method, whose value is independent on any initial estimation, is used to resolve the ill-posed problem of the tomography system. Second, taking into account the "approximation" of its solution, an iterative improvement process of the solution is then implemented by utilizing the conventional algebraic reconstruction algorithm (ART). The HRA, therefore, offers a more reasonable approach to choose an initial approximate for the ART and to improve the quality of the final reconstructed image. A simulated experiment demonstrates that the HRA method is superior to the TSVD or the ART alone for the tomographic inversion of LED. Finally, the HRA is used to perform GPS-based tomographic reconstruction of the IED at mid- and low-latitude regions. [ABSTRACT FROM AUTHOR]

Band selection (BS) is a special case of the feature selection problem, and it tries to remove redundant bands and select a few informative and distinctive bands to represent the whole image cube. The maximum ellipsoid volume (MEV) method regards the band subset with the maximum volume as the optimal band combination. However, the MEV method cannot be directly applied for hyperspectral imagery due to the high dimensionality of the data sets. Therefore, we first combine MEV with the sequential forward search (SFS) and propose a new unsupervised BS method called MEV-SFS. Furthermore, a subtle relationship between the ellipsoid volume of the band set and the orthogonal projections (OPs) of the candidate bands is observed. Based on this relationship, we propose another equivalent method, namely, the OP-based BS (OPBS) method. OPBS is the fast version of MEV-SFS, and it has a better computational efficiency and the potential to determine the number of bands to be selected. We specifically explain the rationality of the MEV-based methods (MEV-SFS and OPBS) and illustrate their theoretical significance and physical meaning from different aspects. Theoretical analysis also demonstrates that OPBS can be regarded as a model or framework for BS, and thus, we further propose a third novel BS method named the OPBS-information divergence (OPBS-ID) method, which is a variant of OPBS. OPBS-ID can achieve a better classification performance than OPBS in many cases. Experimental results on different hyperspectral data sets demonstrate that the proposed methods have high computational efficiency, and the selected bands can achieve satisfactory classification performances. [ABSTRACT FROM AUTHOR]

Operating at L-band (~24 cm wavelength) in wide-swath modes is one of the characteristics of the new and next generation satellite synthetic aperture radar (SAR) missions. After 3 years of operation, the Japan Aerospace Exploration Agency Advanced Land Observing Satellite-2 (ALOS-2) satellite has acquired a wealth of L-band wide-swath SAR data over many areas using its ScanSAR mode. We present interferometry SAR (InSAR) time series analysis results from ALOS-2 ScanSAR data. We analyze the possible error sources in the InSAR and correct them if possible. We present different time series analysis results including azimuth frequency modulation rate error, line of sight (LOS) ionospheric phase, azimuth shift caused by the ionosphere, and LOS displacement processed using both full-aperture and burst-by -burst workflows. The final InSAR LOS displacement time series result reveals both large-scale tectonic and small-scale anthropogenic deformation components. The results demonstrate the potential for measuring continental or even global-scale tectonic deformation and illustrate the promise of upcoming L-band wide-swath SAR missions, such as the NASA-ISRO SAR mission. [ABSTRACT FROM AUTHOR]