IEEE Transactions on Geoscience and Remote Sensing

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Front Cover

Fri, 03/01/2019 - 00:00
Presents the front cover for this issue of the publication.

IEEE Transactions on Geoscience and Remote Sensing publication information

Fri, 03/01/2019 - 00:00
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Table of contents

Fri, 03/01/2019 - 00:00
Presents the table of contents for this issue of the publication.

Collaborative Compressive Radar Imaging With Saliency Priors

Fri, 03/01/2019 - 00:00
Although several works have been done on high-resolution inverse synthetic aperture radar (ISAR) imaging via compressive sampling technology, they only explore sparse priors of targets in the scene and the image is recovered cell by cell separately. In order to potentially enhance targets and suppress clutters for fast and higher quality imaging, in this paper, we advance a collaborative compressive ISAR (CC-ISAR) imaging approach, by exploring both sparse priors and saliency priors of targets. First, a geometric saliency map is derived by performing pulse contourlet transform on a preliminary image. Then, a graph Laplacian is constructed to regularize a multiple measurement vector problem for collaborative compressive radar imaging. Third, targets are approximately separated from the background in the saliency map, and salient weights are defined for the target and background, respectively, to derive a saliency weighted $l_{1}$ -norm optimization algorithm. Some experiments are taken on real ISAR data to evaluate the performance of the proposed method, and both visual results and numerical guidelines prove that CC-ISAR imaging method can obtain more accurate targets and outperform its counterparts.

Gaussian Process Regression for Arctic Coastal Erosion Forecasting

Fri, 03/01/2019 - 00:00
Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shore-fast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern since a large proportion of the population centers and infrastructure in the Arctic are located near the coasts. Stakeholders and decision makers increasingly need models capable of scenario-based predictions to assess and mitigate the effects of coastal morphology on infrastructure and land use. Our research uses Gaussian process (GP) models to forecast Arctic coastal erosion along the Beaufort Sea near Drew Point, AK, USA. GP regression is a data-driven modeling methodology capable of extracting patterns and trends from data-sparse environments such as remote Arctic coastlines. To train our model, we use annual coastline positions and near-shore summer temperature averages from existing data sets and extend these data by extracting additional coastlines from satellite imagery. We combine our calibrated models with future climate models to generate a range of plausible future erosion scenarios. Our results show that the GP methodology substantially improves yearly predictions compared to linear and nonlinear least squares methods and is capable of generating detailed forecasts suitable for the use by decision makers.

A Global Weighted Least-Squares Optimization Framework for Speckle Filtering of PolSAR Imagery

Fri, 03/01/2019 - 00:00
This paper presents a global weighted least-squares (GWLS) optimization framework for polarimetric synthetic aperture radar (PolSAR) despeckling. GWLS is simpler than other optimization methods because it does not lead to complex optimization and iterative convergence problems. Solving the PolSAR despeckling problem based on GWLS is equivalent to solving nine sparse linear systems. First, the guidance image is constructed by the span image of the PolSAR data to calculate the five-point spatially inhomogeneous Laplacian matrix. Next, the weighted sum of the Laplacian matrix and an identity matrix is used to construct a coefficient matrix for the nine linear systems. Finally, each speckle-free channel of PolSAR data is reconstructed equally and globally by solving the linear systems with the same coefficient matrix. Filtering each element of the coherency matrix equally preserves the scattering property inherent in PolSAR data. The performance of the GWLS-based method is demonstrated by both simulated and real PolSAR data. Refined Lee filter, intensity-driven adaptive-neighborhood, improved sigma filter, and nonlocal pretest filter are used in qualitative and quantitative comparison. The experiments show that the proposed method can reach a good tradeoff between noise suppressing and detail preservation and has relatively high processing efficiency.

Adaptive Seabed Characterization With Hierarchical Bayesian Modeling of SAS Imagery

Fri, 03/01/2019 - 00:00
Seabed characterization has utility for numerous applications that seek to explore and interact with the seafloor, ranging from coastal habitat monitoring and subbottom profiling to man-made object detection. In this paper, we characterize seabeds based on the texture patterns within synthetic aperture SONAR (SAS) images constructed from high-frequency side-scan sonar. Features are measured from the SAS images (e.g., lacunarity, an established texture feature coding method, and a circularly shifted histogram of oriented gradients). Based on these SAS image features, we perform unsupervised clustering with a hierarchical Bayesian model, which creates categories of seabed textures. Our clustering algorithm is a new variant of the hierarchical Dirichlet process that is both adaptive to changes in seabeds and processes batches of SAS imagery in an online fashion to learn new seabed types as they are encountered. This allows observations to be clustered, as each batch is processed rather than only after all data have been collected. The model’s performance of seabed characterization by SAS image texture is demonstrated in the overall range and internal consistency of textures specific to each learned cluster with data across a variety of sites.

Hyperspectral Image Classification With Squeeze Multibias Network

Fri, 03/01/2019 - 00:00
A convolutional neural network (CNN) has recently demonstrated its outstanding capability for the classification of hyperspectral images (HSIs). Typical CNN-based methods usually adopt image patches as inputs to the network. However, a fixed-size image patch in HSI with complex spatial contexts may contain multiple ground objects of different classes, which will deteriorate the classification performance of the CNN. In addition, traditional convolutional layers adopted in the CNN have a huge amount of parameters needed to be tuned, which will cause high computational cost. To address the above-mentioned issues, a novel squeeze multibias network (SMBN) is proposed for HSI classification. Specifically, the proposed SMBN first introduces the multibias module (MBM), which incorporates multibias into the rectified linear unit layers. The MBM can decouple the feature maps of input patches into multiple response maps (corresponding to different ground objects) and adaptively select the meaningful maps for classification. Furthermore, the proposed SMBN replaces the traditional convolutional layer with a squeeze convolution module, which can greatly reduce the number of parameters in the network, thus saving the running time, while still maintaining high classification accuracy. Experimental results on three real HSIs demonstrate the superiority of the proposed SMBN method over several state-of-the-art classification approaches.

A Quantitative Analytical Framework for Photon Transfer Curve-Based Preflight Characterization of the Indian Remote Sensing Imaging Systems

Fri, 03/01/2019 - 00:00
Preflight performance characterization of spaceborne imaging systems offers high-quality images for scientific applications. A key challenge in the laboratory characterization process is to identify the candidate signal chains requiring performance optimization. For the Indian Remote Sensing (IRS) imaging systems, light transfer characterization has been identified as a standard process for performance characterization. Photon transfer curve (PTC) is another powerful and widely used tool to characterize the imaging systems in terms of camera gain ( $e^{-}$ /DN), read noise ( $e^{-}$ ), charge-to-voltage conversion factor (CVF), signal-to-noise ratio, mean–variance linearity, and so on. There have been no detailed investigations on the PTC characteristics of the IRS imaging systems. We present here PTC-based characterization studies on two high-resolution IRS imaging systems, namely, Cartosat-1 and Cartosat-2. For this, a quantitative analytical framework has been developed, which enables comparative studies among multiple signal chains by applying various statistical measures on the PTC derived parameters. This framework provides not only a quantitative assessment of performance deviations but also enables performance traceability up to detector level. Taken together, our analysis shows that all the signal chains have well behaved PTC characteristics, and performance deviations are less than 10%. In particular, performance traceability is established by the close match of the system-level CVF values within the detector manufacturer’s specified range. Studies on the adequacy of linear approximation of the PTC curve reveal large residual errors in the lower dynamic range due to an increase in read noise floor. The analytical framework developed here can significantly help- optimize future IRS imaging systems.

An Illumination-Invariant Change Detection Method Based on Disparity Saliency Map for Multitemporal Optical Remotely Sensed Images

Fri, 03/01/2019 - 00:00
Multitemporal airborne and satellite imagery data with frequent repeat coverage provide great capability for change detection (CD). When comparing two images taken at different times of day or in different seasons for CD, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective CD algorithm, therefore, has to be robust to the illumination variation. In this paper, the robustness of phase correlation (PC) to shadow effects is proven via mathematical analysis, and then, an illumination-invariant change detection (IICD) metric is proposed based on pixel-wise dense PC matching. In the proposed IICD method, a graph-based visual saliency map is introduced for the initial CD followed by an active contour-based segmentation to precisely quantize the change region. Compared to the state-of-the-art CD algorithms, experiments using daily images of a landscape model and Landsat satellite images demonstrate that only the proposed method can effectively detect and precisely segment appearance changes under daily and seasonal sunlight changes.

Reconstruction From Multispectral to Hyperspectral Image Using Spectral Library-Based Dictionary Learning

Fri, 03/01/2019 - 00:00
High-spatial hyperspectral (HH) image reconstruction using both high-spatial multispectral (HM) image and low-spatial hyperspectral (LH) image over the same scene is widely used in many real applications. Nevertheless, the pair of HM image and LH image over the same scene is hard to obtain. To solve this problem, a new HH image reconstruction method using spectral library-based dictionary learning (named as HIRSL) is proposed in this paper, only from one HM image. The above reconstruction problem is formulated in the framework of sparse representation, as an estimation of the band matching matrix, the spectral dictionary, and the sparse coefficients. More specifically, a band matching method is proposed for mapping the common spectral library to a specific spectral library corresponding to the reconstructed HH image in spectral domain. Then, an efficient spectral dictionary learning method is proposed for the construction of spectral dictionary using the matched specific spectral library, which avoids the dependence of the LH image over the same scene. Finally, the sparse coefficients of the HM image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers without nonnegative constraint. Comparison results on simulated and real data sets with the relative state-of-the-art methods demonstrate that even only using one HM image, our proposed method achieves a comparable reconstruction quality of high-spatial hyperspectral image both in spatial and spectral domains.

Stem–Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data

Fri, 03/01/2019 - 00:00
Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem–leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentation, which are sensitive to illumination and occlusion. Light detection and ranging (LiDAR) can obtain accurate 3-D information with its active laser scanning and strong penetration ability, which breaks through phenotyping from 2-D to 3-D. However, few researches have addressed the problem of the LiDAR-based stem–leaf segmentation. In this paper, we proposed a median normalized-vector growth (MNVG) algorithm, which can segment stem and leaf with four steps, i.e., preprocessing, stem growth, leaf growth, and postprocessing. The MNVG method was tested by 30 maize samples with different heights, compactness, leaf numbers, and densities from three growing stages. Moreover, phenotypic traits at leaf, stem, and individual levels were extracted with the truly segmented instances. The mean accuracy of segmentation at point level in terms of the recall, precision, F-score, and overall accuracy were 0.92, 0.93, 0.92, and 0.93, respectively. The accuracy of phenotypic trait extraction in leaf, stem, and individual levels ranged from 0.81 to 0.95, 0.64 to 0.97, and 0.96 to 1, respectively. To our knowledge, this paper proposed the first LiDAR-based stem–leaf segmentation and phenotypic trait extraction method in agriculture field, which may contribute to the study of LiDAR-based plant phonemics and precise agriculture.

A Statistical Inverse Method for Gridding Passive Microwave Data With Mixed Measurements

Fri, 03/01/2019 - 00:00
When a passive microwave footprint intersects objects on the ground with different spectral characteristics, the corresponding observation is mixed. The retrieval of geophysical parameters is limited by this mixture. We propose to partition the study region into objects following an object-based image analysis procedure and then to refine this partition into small cells. Then, we introduce a statistical method to estimate the brightness temperature (TB) of each cell. The method assumes that TB of the cells corresponding to the same object is identically distributed and that the TB heterogeneity within each cell can be neglected. The implementation is based on an iterative expectation–maximization algorithm. We evaluated the proposed method using synthetic images and applied it to grid the TBs of sample AMSR –2 real data over a coastal region in Argentina.

A Selection Criterion for the Optimal Resolution of Ground-Based Remote Sensing Cloud Images for Cloud Classification

Fri, 03/01/2019 - 00:00
In ground-based remote sensing cloud image observation, images with the highest possible resolution are captured to obtain sufficient information about clouds. However, when features are extracted and classification is performed on the basis of the original images, a high-resolution probably means a high (or even more, unacceptable) computation cost. In practical application, a simple and commonly adopted method is to appropriately resize the original image to a version with a decreased resolution. An inevitable problem is whether useful information is lost in this resizing operation. This paper demonstrates that information loss is inevitable and poor classification results may be obtained from the analysis of local binary pattern (LBP) histogram features. However, this problem has been always neglected in previous studies, and the original image is arbitrarily resized without any criterion. In particular, the histogram features based on LBPs actually reflect the distribution of features. Thus, a criterion based on the Kullback–Leibler divergence between LBP histograms from the original and resized images and a penalty term imposed on the resolution are proposed to select the resolution of the resized image. The optimal resolution of the resized image can be selected by minimizing this criterion. Furthermore, experiments based on three ground-based remote sensing cloud image data sets with different original resolutions validate this criterion by analyzing the LBP histogram features.

Large-Scale Planar Block Adjustment of GaoFen1 WFV Images Covering Most of Mainland China

Fri, 03/01/2019 - 00:00
GaoFen1 is the first high-resolution earth observation satellite built in China, and carries four wide field-of-view (WFV) cameras to achieve large-scale monitoring and mapping. However, the unstable attitude measurement accuracy of the satellite generally imparts low geopositioning accuracy and inconsistent geometric error in overlapping areas of WFV images. A feasible and effective large-scale planar block adjustment (PBA) method is presented that corrects the geometric errors of the vast WFV images integrally, further improving the geometric accuracy of these images. In addition, whether ground control points (GCPs) are needed, and the effect of different numbers of GCPs on PBA accuracy is also investigated. Two key technologies are used in this paper. First, a universal PBA error equation based on the virtual control points is presented to allow PBA with or without GCPs. Second, an adjustment method aided by a digital elevation model is adopted to overcome the weak convergence geometry among WFV images, further ensuring stable estimation of PBA. The effectiveness of the proposed method was verified by 664 WFV images covering most of mainland China. The satisfactory experimental results indicate that the method presented herein is reasonable and effective, but that a certain number of GCPs is needed to ensure the accuracy of large-scale PBA results for WFV images.

Detecting Changes in Fully Polarimetric SAR Imagery With Statistical Information Theory

Fri, 03/01/2019 - 00:00
Images obtained from coherent illumination processes are contaminated with speckle. A prominent example of such imagery systems is the polarimetric synthetic aperture radar (PolSAR). For such a remote sensing tool, the speckle interference pattern appears in the form of a positive-definite Hermitian matrix, which requires specialized models and makes change detection a hard task. The scaled complex Wishart distribution is a widely used model for PolSAR images. Such a distribution is defined by two parameters: the number of looks and the complex covariance matrix. The last parameter contains all the necessary information to characterize the backscattered data, and thus, identifying changes in a sequence of images can be formulated as a problem of verifying whether the complex covariance matrices differ at two or more takes. This paper proposes a comparison between a classical change detection method based on the likelihood ratio and three statistical methods that depend on information-theoretic measures: the Kullback–Leibler (KL) distance and two entropies. The performance of these four tests was quantified in terms of their sample test powers and sizes using simulated data. The tests are then applied to actual PolSAR data. The results provide evidence that tests based on entropies may outperform those based on the KL distance and likelihood ratio statistics.

Joint Sparsity-Based Imaging and Motion Error Estimation for BFSAR

Fri, 03/01/2019 - 00:00
Due to its flexibility and low cost, the bistatic forward-looking synthetic aperture radar (BFSAR) which employs side-looking transmitter and forward-looking receiver has been studied in recent years. Sparsity-based techniques have been applied in the field of BFSAR imaging and show great potential. In sparsity-based BFSAR imaging, compensation of the motion errors is crucial to get a well-focused image. For fields that admit a sparse representation, we propose a sparsity-based imaging approach integrated with motion error estimation and compensation in this paper. First, a novel joint phase-amplitude compensation-based motion error correction scheme is developed to cope with the spatial variance of motion error. Then, an inversion observation model of the range-Doppler algorithm combined with motion error correction is derived, based on which a joint problem of BFSAR imaging and motion error estimation is formulated as a sparse recovery problem and solved in an iterative way, where in each iteration, both image formation and motion error correction are carried out. Experiments on both the simulated and real BFSAR data show that the proposed method can obtain a more accurate estimation result, and generate better focused images compared with the existing methods.

Hyperspectral Image Restoration Based on Low-Rank Recovery With a Local Neighborhood Weighted Spectral–Spatial Total Variation Model

Fri, 03/01/2019 - 00:00
Hyperspectral image (HSI) is often contaminated by mixed noise, which severely affects the visual quality and subsequent applications of the data. In this paper, HSI restoration based on low-rank recovery with a local neighborhood weighted spectral–spatial total variation (TV) model is proposed, which focuses on the preservation of spatial structure and spectral fidelity. The low-rank matrix model is adopted to exploit the spectral and spatial correlation information, and the $l_{1}$ -norm is used as a prior to remove the sparse noise. Furthermore, a local spatial neighborhood weighted spectral–spatial TV is utilized to jointly model the spectral–spatial prior information; specifically, the spectral and spatial differences are both considered in the TV term, and the weight is computed by considering the local neighborhood information in the spatial domain. Alternating direction method of multipliers optimization procedure is extended to solve the presented model. Experimental results demonstrate that the proposed method can remove the mixed noise, enhance the structural information simultaneously, and offer the best performance compared with several state-of-the-art HSI restoration methods.

Normalized Difference Latent Heat Index for Remote Sensing of Land Surface Energy Fluxes

Fri, 03/01/2019 - 00:00
Latent heat is the energy released or absorbed by a substance through phase change without changing its temperature. Its flux is a crucial element of the hydrological cycle at the land–air interface. Many water-related indexes have been proposed as indicators for latent heat flux extraction from satellite imagery, while the extraction accuracy still remains a space to improve nowadays. In this paper, a new multiband index, called normalized difference latent heat index (NDLI), is proposed for remote sensing of land surface heat flux. It utilizes the reflectance observations of three bands from Landsat 8 operational land imager, including red, green, and shortwave infrared channels. Its performance in terms of deriving latent heat flux through their incorporation into the commonly used surface energy balance algorithm for land (SEBAL) is then compared with the other three existing water-related indexes with two of them using normalized difference water index and normalized difference vegetation index. Results show that NDLI exhibits the strongest correlation with the SEBAL-derived latent heat flux among the used water-related indexes with correlation coefficient $r = 0.75$ . They also indicate that the NDLI is the most sensitive and reliable index outperforming the previously developed indexes to determine the characteristics of water content in different land cover types. It is concluded that NDLI can be used as a good indicator to represent the potential latent heat flux at the earth’s surface.

Sparsity-Driven GMTI Processing Framework With Multichannel SAR

Fri, 03/01/2019 - 00:00
This paper presents a processing framework to separate moving targets from the clutter, under multichannel synthetic aperture radar (SAR) scenarios, and addresses the moving target imaging and velocity estimation problems for ground moving target indication (GMTI) applications. A practical implementation is introduced to break the SAR/GMTI problem into two processing stages, and the sparsity of the moving targets in the observed scene is exploited throughout the stages. The two-stage process extracts the moving targets from the monitored region via a sparsity-based iterative decomposition algorithm and subsequently estimates the complete velocity vectors of moving targets by enforcing sparsity constraints. The model is sufficiently versatile to incorporate digital elevation map information, which further improves the moving target relocation accuracy. The effectiveness of the presented framework is demonstrated using the Air Force Research Laboratory Gotcha GMTI challenge data.

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