Multibaseline interferometric synthetic aperture radar (InSAR), multibaseline polarimetric InSAR (PolInSAR), and SAR tomography (TomoSAR) are the advanced techniques for forest height inversion. However, accurate large-scale inversion using these techniques still faces two key problems: 1) for InSAR and PolInSAR, the existing inversion models fail to accurately describe the vertical structure, reducing the inversion performance; 2) for TomoSAR, its baseline configuration requirement is too high to reconstruct the vertical structure of the large-scale forested area. To solve these two problems, this paper proposes a high inversion accuracy forest vertical structure heterogeneity (FVSH) coherent scattering model and a small-scale tomography-aided multibaseline (Pol)InSAR (STMI) forest height inversion framework, which provide a synergic observation and inversion scheme of these multibaseline SAR techniques using machine learning approaches. The different-frequency InSAR and PolInSAR data acquired above the different types of forested areas are selected for validation. The experimental results show the effectiveness of the proposed framework.

Polarimetric calibration (PolCal) of polarimetric synthetic aperture radar (PolSAR) images is essential for quantitative remote sensing applications. The distributed targets like forest, provide polarimetric constrains for PolCal, for example, reflection symmetry constrain. In this paper, we find that the helix scattering has a negative relationship with reflection symmetric distributed targets and is useful in PolCal. We first examined the tolerance of helix-ratio feature to polarimetric distortions, and then developed an algorithm to calibrate PolSAR images with this feature. Experiments with Chinese X-band airborne images show that PolCal with helix-ratio can improve the utilizing of distributed targets, and can improve the crosstalk accuracy.

Nowadays, non-corner reflectors (CRs) calibration techniques are attractive to relieve the workload of polarimetric calibration. Yet, it is challenging to derive the complex co-pol channel imbalance (CCI) precisely and conveniently when CRs are unavailable. Our previous research provided the general unitary zero helix (gUZH) and phase-optimized UZH (pUZH) methods to estimate CCI by the bare soil pixels. However, the amplitude is still overestimated compared with CRs. Thus, this paper proposes to optimize the goal function by L2 normalization and derive the amplitude-optimized UZH (aUZH) to estimate the CCI amplitude robustly. The new aUZH performs well in resisting errors from improper reference picking. We also develop a signal-to-noise ratio (SNR) filter that selects the soil pixels with high SNR into aUZH to reduce the influence of the noise floor. Furthermore, we combine aUZH and the SNR filter to develop a method, i.e., HybridC, to process the massive data for a more precise solution. This paper validates the new algorithm through the external calibration of the Gaofen-3 satellite from 2017 to 2020. The result shows that CR error in HH/VV is better than 0.26 dB after aUZH calibration. Furthermore, we process the proposed HybridC as a tool to monitor the sensor quality of the Gaofen-3 in over 40,000 images. We find that the imbalance phase exceeds the designed specification at some beams and that our algorithm can calibrate the bias precisely.

The deep convolutional neural network (CNN) has been extensively applied to polarimetric synthetic radar (PolSAR) imagery classification. However, its success is greatly dependent on numerous labeled samples for revealing and modeling the characteristics of different targets, thus remaining a challenge in maintaining high accuracy in limited sample cases. To address this issue, a deep reinforcement learning (RL)-based PolSAR image classification framework, named deep Q-fully CNN (DQFCN), is proposed in this article. In this framework, two ways are adopted to boost the classification performance while reducing training samples. On the information utilization hand, the spatial neighboring information and polarimetric decomposition information of PolSAR data are both extracted to enrich the feature representation of the sample. Meanwhile, the 3-D CNN architecture is adopted to learn the spatial-polarimetric jointed characteristics simultaneously. On the model learning hand, two RL learning strategies are employed to promote classification performance. The first one is learning from scratch, which does not use any label information as prior knowledge but learns from its self-generated experience. Learning from pretraining is the second strategy in which the networks are sequentially trained from labeled samples and experience data to reduce the time cost. As far as we know, it is the first time that an RL-based fully CNN has been proposed for PolSAR image classification. Experiments on three benchmark datasets prove the effectiveness of the proposed framework, suggesting that the two adopted strategies achieve boosted performance in all experiments, particularly in a limited sample size.

The Chinese Gaofen-3 satellite currently provides us with an open way to access fully polarimetric data in the C-band frequency. The noise equivalent sigma zero (NESZ) is a crucial factor when calibrating the additive noise in radar imagery. For most radar sensors, NESZ coefficients are stored in header files, but these are not provided for the Gaofen-3 products. The minimum eigenvalue estimator (MEE) and maximum likelihood estimator (MLE) are the two most common techniques used to derive the NESZ from polarimetric imagery. Nevertheless, the bias has been found to be higher than 5 dB compared with the noise measurement circuit (NMC) of the hardware. In this article, we propose a minimum noise envelope estimator (MNEE) for the robust estimation of the Gaofen-3 NESZ. In this article, we carried out an in-depth investigation to analyze the error sources of the MEE and MLE techniques. Based on our analysis, the MNEE framework requires the use of the ocean surface as a reference, and MNEE is combined with the minimum operation to suppress overestimation. In the experimental section, we describe how we validated the proposed algorithm with Radarsat-2 images, and the MNEE is treated as a tool to estimate the NESZ of Gaofen-3 polarimetric products. We found that the Gaofen-3 NESZ is generally less than -20 dB, which satisfies the design specification. The range-dependent NESZ coefficients are provided here to allow convenient noise correction for Gaofen-3 data users.

Clustering is a common method for hyperspectral image (HSI) interpretation in the case of no labeled samples. Many subspace clustering methods have now been proposed for HSIs and have obtained remarkable success. However, because of the prohibitively large computational complexity induced by the self-dictionary representation, these methods suffer from the scalability issue and are ineffective for large HSIs. In this article, to address this issue, we focus on a scalable subspace clustering scheme and introduce the recently developed sketched subspace clustering (sketched-SC) model to HSI. The sketched-SC model is computationally inexpensive and is suitable for the large HSI clustering task as it constructs a compact yet expressive dictionary. However, several problems degrade the performance of sketched-SC, i.e., the inadequate mining of the structural information and no consideration of spatial information. In view of this, a novel scalable nonlocal means regularized sketched reweighted sparse and low-rank (NL-SSLR) SC algorithm is proposed for use with large HSIs. On the one hand, the SSLR representation model is constructed to explore the underlying local and global structural information of the HSIs at the same time. On the other hand, the nonlocal means regularization is used to fully explore the spatial correlation information and better account for the self-similarity of HSIs, to further boost the clustering performance. The experimental results obtained on two well-known hyperspectral data sets corroborate the superiority of the proposed algorithm over the other state-of-the-art HSI clustering methods.

Hyperspectral image (HSI) clustering is extremely challenging because of the complexity of the image structure. Recently, the subspace clustering algorithms have achieved competitive performance for HSIs. However, these methods generally are computationally complex and time-and-memory-consuming, given their reliance on large-scale adjacency matrix learning and graph segmentation, which limits their application to large HSIs and reduces their attractiveness in real applications. In this article, in view of this, two novel sparsity-based clustering algorithms are proposed for large HSIs, named sparse coding-based clustering (SCC) and joint SCC (JSCC). To the best of our knowledge, we are the first to use the sparse representation recovery residual to cluster HSIs. Based on a structured dictionary constructed by$k$-means and$k$-nearest neighbor (KNN), an SCC model is constructed to cluster HSIs according to the recovery residual minimization criterion. By dealing with a pixel-wise sparse recovery problem instead of the large-scale graph optimization problem of the whole image, the computational complexity and the time-and-memory cost are reduced to a large degree, which makes sense for practical applications. Then, by introducing the super-pixel neighborhood, a JSCC model is constructed to better explore the interpixel correlation of HSIs and further improve the clustering performance. The proposed algorithms were verified on three widely used HSIs. All the three experiments confirm the effectiveness of the proposed algorithms, which can be considered as competitive tools for use with large HSIs.

For forest with complex structure, the vertical structure backscatter is influenced by a combination of factors, including the frequencies of the radar waves and the forest biophysical parameters (i.e., density, species). The backscatter-induced error is thus a critical element in limiting the accuracy of polarimetric synthetic aperture radar interferometry (Pol-InSAR) forest height inversion based on a single model. It is, therefore, necessary to select the optimal backscatter profile from the multiple possible solutions in each pixel of the test site. In this letter, the impacts of backscatter in forest height estimation based on the models of random volume over ground (RVoG) (σ > 0), RVoG (σ <; 0), and Gaussian vertical backscatter (GVB) were investigated in the complex plane, and then with the combined use of Pol-InSAR and light detection and ranging (LiDAR), a random forest (RF) classifier is trained to obtain the optimal backscatter function and Pol-InSAR forest height from the results based on the different models in each pixel. The proposed method was tested with single- and multi baseline Pol-InSAR data in the P-band, and the root-mean-square errors (RMSEs) of the proposed approach were 2.85 and 2.69 m, respectively, which represented average improvements of 20.6% and 17.7% over the optimal single-model inversion.

In this article, we propose a polarimetric calibration (PolCal) algorithm to estimate the system crosstalk, cross-polarization (x-pol), and co-polarization (co-pol) channel imbalance (CI) when ground corner reflectors (CRs) are unavailable. The current PolCal process requires at least one trihedral CR to determine the co-pol CI. However, the deployment of ground CRs is costly and may even be impossible in some areas. To calibrate a polarimetric image without CRs, our proposed method automatically extracts the volume-dominated and Bragg-like pixels as a reference to estimate the crosstalk, x-pol, and co-pol CI values. Then, a first-order polynomial model is exploited to fit the co-pol CI to further improve calibration accuracy. In the experimental section, we demonstrate the effectiveness of our proposed method with data from two of China's newly developed very high-resolution systems. The experiments confirmed that the proposed workflow can be considered as a feasible calibration scheme when the ground deployment of CRs is impossible, and it is also an effective analysis tool for the assessment of calibrated products.

The quick and accurate extraction of water bodies from images is imperative for land resources management, ecological protection, and flood disaster prevention. However, the prevalent methods for water body extraction of SAR imagery have the major issues of depending on expert experience, the poor retention of water-land boundaries, and a high false alarm rate. In this paper, focusing on these issues, we study the effectiveness of water body extraction using only simple polarimetric decomposition components and commonly used machine learning classifiers for Gaofen-3(GF-3) image. We test it using different sizes of training samples and compare it with the previous methods. The experimental results showed that the combination of polarimetric decomposition components and machine learning classifiers can achieve satisfactory accuracies in water body extraction of GF-3 image, and has strong robustness, reliability and high application value.

A modified model-based polarimetric decomposition method, considering both the surface and dihedral scattering depolarization, is proposed for soil moisture retrieval. In the parameter solution, it works combining at least two polarimetric SAR images acquired simultaneously in different incidence angles. Moreover, it needs not to decide whether dihedral or surface scatter is the dominant contribution. The experiments to demonstrate the potential of the proposed approach is carried out using L-band polarimetric UAVSAR multi-incidence data in Winnipeg, Canada. The scattering mechanism of forest, grass land, urban, and barren area are analyzed compared with Yamaguchi three-component decomposition results. The performance of the soil moisture estimation algorithm is also assessed by comparing the retrieval results with in situ measurements.

This letter presents two main contributions to the data quality assessment of China's new GaoFen-3 radar satellite. First, we observed a half-pixel misregistration between the horizontal (H) and vertical (V) transmitting channels in the azimuth direction. This was determined by investigating the corner reflector (CR) response of some stripmap products of GaoFen-3 quad-pol stripmap (QPSI) mode. Second, to check whether the azimuth misregistration exists in different beams of QPSI mode, we improved the RADARSAT-2 channel checking method as a tool and evaluated more than 300 stripmap scenes. This letter confirms that the half-pixel misregistration problem, which can cause about 10% decoherence in the co-pol and cross-pol channel correlation coefficients, is common in the GaoFen-3 stripmap products of QPSI mode. Furthermore, the improved method can be considered as an effective way to fix the misregistration problem.

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.

This paper presents a spherically invariant random vector (SIRV)-based dual-domain filter for polarimetric synthetic aperture radar (PolSAR) speckle suppression. The SIRV model is a simple clutter decomposition model designed for heterogeneous scenes that decomposes the clutter into two independent domains: the texture domain and the polarimetric domain (also known as the speckle domain). The dual-domain filter takes full advantage of the SIRV model to decompose the task of PolSAR speckle suppression into polarimetric domain filtering and texture domain filtering. The polarimetric domain filtering involves estimating a stable state of the normalized coherency matrix with similar samples via fixed-point iteration. For this purpose, the nonlocal patch matching distance measure and the cutoff threshold in the SIRV model case are defined to select similar samples. The texture domain filtering involves reconstructing a sparse texture image without the effect of speckle. For this purpose, patch ordering-based SAR image despeckling via transform-domain filtering (SAR-POTDF), which is based on simultaneous sparse coding (SSC), is applied for the texture filtering. After the dual-domain filtering, a speckle-free PolSAR image can be reconstructed by the product of the texture and the normalized coherency matrix. The effectiveness and robustness of the proposed method was demonstrated by experiments undertaken with CETC-38 X-band, DLR F-SAR S-band, and IECAS C-band high-resolution PolSAR images. The DLR E-SAR L-band medium-resolution data were also used for comparison. The results showed that the dual-domain filtering is effective for the speckle suppression of high-resolution PolSAR images with heterogeneous and detailed scenes.

Clustering is a very challenging task for hyperspectral imagery (HSI) because of the complex spectral-spatial structures found in such data. Recently, the sparse recovery-based approaches have been introduced to deal with hyperspectral clustering, and have achieved state-of-the-art performances. Several recent works have shown that it is the collaborative representation mechanism over all the dictionary atoms, rather than the sparse constraint that determines the recognition performance. Based on this fact, in this paper, we focus on the working mechanism of collaborative representation to explore its potential in HSI clustering. However, directly introducing collaborative representation clustering (CRC) to HSIs results in several problems, i.e., the high redundancy of the global dictionary atoms and the absence of spatial information, which greatly limit the clustering performance. In view of this, we propose a novel total variation regularized CRC with a locally adaptive dictionary (TV-CRC-LAD) algorithm for HSI. First, the LAD construction strategy is introduced instead of the global dictionary to relieve the high redundancy and the interference of unrelated atoms in the representation process, to more precisely represent each pixel only with the highly correlated atoms. Second, TV regularization is integrated to better account for the rich spatial-contextual information and promotes the piecewise smoothness of the HSI clustering result. The proposed algorithm was tested on three widely used hyperspectral data sets, and the experimental results clearly illustrate that the proposed algorithm outperforms the corresponding sparsity-based clustering methods and the other state-of-the-art methods.

Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. Recently, sparse representation (SR) theory has been successfully introduced to HSI band selection, and many SR-based methods have been developed and shown great potential and superiority. However, due to the inherent limitations of the SR scheme, i.e., individually representing each band with only a few other bands from the same subspace, the SR-based methods cannot effectively capture the global structures of the data, which limit the band selection performance. In this paper, to overcome this obstacle, the novel Laplacian-regularized low-rank subspace clustering (LLRSC) algorithm is proposed for HSI band selection. On the one hand, the low-rank subspace clustering model is introduced to capture the global structure information for the learned representation coefficient matrix and deal with the HSI band selection task in the clustering framework. On the other hand, considering the high correlation between adjacent bands, 1-D Laplacian regularization is utilized to incorporate the neighboring band information and further reduce the representation bias. Lastly, an eigenvalue analysis algorithm based on band mutation information is utilized to estimate the appropriate size of the band subset. The experimental results indicate that the proposed LLRSC algorithm outperforms the other state-of-the-art methods and achieves a very competitive band selection performance for HSIs.

The periodic surface are often seen in agriculture, such as the planting fields of potato, sugarcane, and green onion. There are strong coherent scattering for the fields whose aligning direction is perpendicular to radar's light of sight (LOS). Enhanced backscattering coefficients induced by coherent scattering on synthetic aperture radar (SAR) images hinder the retrieval of soil moisture. The paper investigated the capability of different retrieval models in the inversion of soil moisture for bare periodic fields. The results show that the copolarized ratio and HV backscattering coefficient, which are less affected by the periodic structure, can be used to reduce the effect of coherent scattering on soil moisture inversion.

The main objective of this paper is to evaluate a time series of Sentinel-1A soil moisture product acquired from March 2015 to November 2016 using global passive ones: SMAP, SMOS and AMSR-2, together with ground measurements at ISMN site Dahra in Senegal. The retrieving algorithm is a LUT method based on simulated data cube using MIMICS and AIEM of VV polarization. Results show that ubRMSD and MAD of soil moisture estimated by 1km Sentinel-1A compared to ground measurements are both below 6%, and most of Pearson correlations between Sentinel-1A and global products are significant, which indicate the great potential of the application of Sentinel-1 data. Comparisons among the global products are also conducted to embody that L band is indeed more suitable for soil moisture estimation than C band.

Recently, with the rapid development of earth observation (EO) techniques, the similar object information has been acquired in different regions, by the use of various sensors. It brings up a new challenge, that is, how to identify cross-domain objects. To cope with this difficulty, we first revisit the linear discriminative Laplacian eigenmaps (LDLE) in this paper, and further add a Bregman divergence (BD) based regularization term into it. The experiment results demonstrate that, the combination of LDLE and BD can learn a good linear transformation of polarimetric synthetic aperture radar (PolSAR) data and improve classification performances.

This paper proposes a scattering model based segmentation technique for polarimetric synthetic aperture radar (PolSAR) images. The method is composed of two main parts: merging predicate and merging order. The merging predicate is based on the idea of the fractal network evolution algorithm (FNEA). The heterogeneity of the scattering characteristics between adjacent regions is calculated to judge whether the two adjacent regions should be merged, and the merging order is determined by the gradient. Experiments using AIRSAR L-band PolSAR data confirm the good segmentation performance of the proposed method.

In this paper, we propose total variation regularized collaborative representation clustering with a locally adaptive dictionary for hyperspectral remote sensing imagery. With regard to the high redundancy of the global dictionary and the interference of unrelated dictionary atoms in the representation process, the collaborative representation clustering model with a locally adaptive dictionary is introduced to more precisely represent each pixel only with highly correlated atoms. In addition, total variation regularization is integrated to better account for the rich spatial contextual information. The extensive experimental results clearly illustrate the superiority of the proposed algorithm.

Accurate and timely change detection of Earth's surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Post-Classification Comparison (PCC) methods based on supervised change detection are widely used in change detection for remote sensing images, but are easily affected by a significant cumulative error of single remote sensing image classification. Unsupervised change detection methods are affected by the speckle noise and cannot explicitly identify the types of land cover or land use transitions. To solve those problems, this paper proposes a change detection method based on similarity measure and joint classification. The similarity measure is obtained by test statistic and Kittler and Illingworth minimum-error thresholding algorithm (TSKI), which is used to automatically control the joint-classification classifier. The efficiency of the proposed method is demonstrated by the polarimetric synthetic aperture radar (PolSAR) images acquired by Radarsat-2 over Wuhan of China. The experimental results show that the method can identify different types of land cover changes and reduce the false alarms in the change detection.

A lodged wheat detection algorithm by applying a false alarm rate to the circular-pol correlation coefficient (CCC) and the total scattered power (Span) is proposed. The CCC is first used to identify non-lodged wheat, which is reflection symmetry. The Span feature is introduced to distinguish lodged wheat from canola in the study area, according to their large difference in scattering intensity. The Polarimertric synthetic aperture radar (PolSAR) image acquired by the Radarsat-2 satellite over the Yigen farmland of China, was used to validate the effectiveness of the proposed approach. The results indicate the potential of using only post-event PolSAR image to detect the lodged wheat.

Land-cover classification in polarimetric synthetic aperture radar images is a vital technique that has been developed for years. The Wishart distribution, which the polarimetric coherence matrix obeys, has been researched to design the well-known Wishart classifier. This model is appropriate for homogeneous scenes, but it usually fails in reality when a category consists of several subcategories or clusters. Therefore, a simple but powerful sample-merging strategy is proposed to generate representative subcenters, based on a dissimilarity measure. In addition, a weighted likelihood-ratio criterion is also proposed to further improve the performance of the Wishart distribution-based classification, based on the Wishart test statistic. Two experiments on EMISAR and UAVSAR data sets confirm that combining the proposed strategies can achieve better results than can the Wishart classifier and the other existing methods.

Robust techniques such as sparse subspace clustering (SSC) have been recently developed for hyperspectral images (HSIs) based on the assumption that pixels belonging to the same land-cover class approximately lie in the same subspace. In order to account for the spatial information contained in HSIs, SSC models incorporating spatial information have become very popular. However, such models are often based on a local averaging constraint, which does not allow for a detailed exploration of the spatial information, thus limiting their discriminative capability and preventing the spatial homogeneity of the clustering results. To address these relevant issues, in this letter, we develop a new and effective ℓ2-norm regularized SSC algorithm which adds a four-neighborhood ℓ2-norm regularizer into the classical SSC model, thus taking full advantage of the spatial-spectral information contained in HSIs. The experimental results confirm the potential of including the spatial information (through the newly added ℓ2-norm regularization term) in the SSC framework, which leads to a significant improvement in the clustering accuracy of SSC when applied to HSIs.

Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. In this paper, a novel squaring weighted low-rank subspace clustering band selection (SWLRSC) algorithm is proposed for hyperspectral imagery. The SWLRSC method can effectively capture the global structure information of the HSI band set by constructing a strongly connected adjacency matrix with accurate representation coefficients, and can adaptively determine an appropriate size for the selected band subset. The experimental results indicate that the proposed SWLRSC algorithm outperforms the state-of-the-art band selection algorithms.

In this paper, we investigate the separability of targets with weak backscattering on synthetic aperture radar (SAR) images by using of an unsupervised classification method. This technique is a combination of the Cloude target decomposition and the likelihood ratio test based on complex Wishart distribution for the polarimetric covariance matrix. The polarimetric SAR (PolSAR) image is initially classified by the H - α plane into eight classes. The dissimilar distance measure is derived from the statistical test of equality of covariance matrices. Significant improvement of the classification results are observed for weak backscattering targets in iterations. The effectiveness of this algorithm is demonstrated using a Radarsat-2 PolSAR image in C band and an ALOS PALSAR PolSAR image in L band.

This study investigates the characteristics of periodic surface in agriculture by the use of C-band polarimetric synthetic aperture radar (PolSAR) imagery. The scattering characteristics of the periodic potato fields in different directions are highlighted using a set of polarimetric parameters. Enhanced coherent scattering is observed when the alignment direction of ridging patterns is perpendicular to radar's line of sight (LOS). There are higher copolarized backscattering coefficients and unaffected cross-polarized backscattering coefficient for the coherent scattering. The increased copolarized correlation coefficient, reduced entropy and polarimetric alpha angle indicate that the induced coherent scattering has small scattering randomness and the odd scattering is its dominant scattering mechanism.

It is well known that polarimetric synthetic aperture radar (PolSAR) backscattering features are highly influenced by the variation of incidence angle (VIA), which usually hampers the classification of most grazing-angle-sensitive targets, such as land and ocean targets. To relieve this issue, various feature extraction approaches have been suggested to enhance the class discriminability while reducing the observed feature dimensionality. The Laplacian eigenmap-based dimension reduction (DR) has been proven to be an effective way to deal with VIA problems, provided that the manifold parameters [e.g., the heat kernel (HK)] have been optimally sought, which is often difficult in practice. In this letter, an adaptive Laplacian eigenmap-based DR method is presented to find a learned subspace where the local geometry with discriminative prior knowledge is preserved as much as possible while near optimal HK and scale factor parameters are automatically identified. The learned feature representation is then employed for the subsequent classification. The improved Laplacian eigenmap algorithm was validated by three uninhabited-aerial-vehicle-synthetic-aperture-radar L-band PolSAR images from the Gulf Deepwater Horizon oil spill, which were clearly impacted by the VIA phenomenon. The experimental results showed that the proposed algorithm works well in ocean target discrimination compared with the current common methods.

Clustering for hyperspectral images (HSIs) is a very challenging task due to its inherent complexity. In this paper, we propose a novel spectral-spatial sparse subspace clustering S4C algorithm for hyperspectral remote sensing images. First, by treating each kind of land-cover class as a subspace, we introduce the sparse subspace clustering (SSC) algorithm to HSIs. Then, considering the spectral and spatial properties of HSIs, the high spectral correlation and rich spatial information of the HSIs are taken into consideration in the SSC model to obtain a more accurate coefficient matrix, which is used to build the adjacent matrix. Finally, spectral clustering is applied to the adjacent matrix to obtain the final clustering result. Several experiments were conducted to illustrate the performance of the proposed S4C algorithm.

In this letter, a comprehensive study of the mapping of building collapse levels by the use of postearthquake synthetic aperture radar (SAR) images is addressed. Although previous studies have successfully quantified the collapse level by the use of postevent SAR, the types of features that are of benefit to the final accuracy still remain unknown. This letter takes the Yushu earthquake as a case study to contribute in two key areas. First, the Chinese dual-band airborne SAR mapping system (CASMSAR), which collected very-high-resolution X-band and P-band images (0.50 and 1.1 m, respectively) by interferometric and polarimetric modes, is comprehensively evaluated for the first time. Second, to discriminate intact and fallen structures, multiconfiguration SAR data features are analyzed, including 40 polarimetric, 3 interferometric, and 138 texture features. Furthermore, a random-forest decision framework is introduced to quantify the importance score of each feature and to improve the discrimination accuracy. The CASMSAR experiment results indicate that the texture is recommended as a powerful input to quantify the extent of building collapse in Yushu.

Filling dead pixels or removing uninteresting objects is often desired in the applications of remotely sensed images. In this paper, an effective image inpainting technology is presented to solve this task, based on multichannel nonlocal total variation. The proposed approach takes advantage of a nonlocal method, which has a superior performance in dealing with textured images and reconstructing large-scale areas. Furthermore, it makes use of the multichannel data of remotely sensed images to achieve spectral coherence for the reconstruction result. To optimize the proposed variation model, a Bregmanized-operator-splitting algorithm is employed. The proposed inpainting algorithm was tested on simulated and real images. The experimental results verify the efficacy of this algorithm.

In this paper, a novel hyperspectral subspace clustering algorithm based on decision fusion strategy (SCDFS) is proposed. Because the different clusters are contained in different subspace of the same hyper-dimensional data, the clustering processing in different subspace is conducted by genetic K-means algorithm (KGA). The clustering results from different subspace can be combined into decision string. The proposed subspace clustering based on decision fusion strategy is conducted on decision string. Considering the selection of subspace, the decision results may be inaccurate. So by the majority voting processing for different subspace, the steady subspace combination can be determined. Finally, the weighted strategy is introduced into SCDFS algorithm to evaluate the distance of different decision string, and determine the fusion clustering result.

The sensitivity of polarimetric parameters to different residue patterns during harvest has been investigated in the paper. Several crop residue cover types called residue patterns during harvest have been selected. They include harvested wheat fields with burned and non-burned straws, the swathed oilseed rapes with different moisture, the rape swathes aligned in different directions, the non-lodging wheat and lodging wheat before harvest. A set of polarimetric parameters include backscattering coefficients, coherence coefficients, polarimetric phase differences, and variables derived from Cloude decomposition are made use of. Sensitivity analysis was conducted on one Radarsat-2 quad-pol image and one TerraSAR-X dual-pol image acquired over a farmland of China. The results indicate that polarimetric parameters have various responses to different residue patterns. The residue amounts, direction and crop harvest state are the main factors that result in the fluctuation of polarimetric scattering characteristics. In addition, it is revealed that multi-polarimetric image with short wavelength has the potential to guide the farming behaviors during harvest.

This letter introduces an efficiency-manifold-learning-based supervised graph embedding (SGE) algorithm for polarimetric synthetic aperture radar (POLSAR) image classification. We use a linear dimensionality reduction technology named SGE to obtain a low-dimensional subspace which can preserve the discriminative information from training samples. Various POLSAR decomposition features are stacked into the input feature cube in the original high-dimensional feature space. The SGE is then implemented to project the input feature into the learned subspace for subsequent classification. The suggested method is validated by the full polarimetric airborne SAR system EMISAR, in Foulum, Denmark. The experiments show that the SGE presents a favorable classification accuracy and the valid components of the multifeature cube are also distinguished.

This letter addresses a statistical model-based decomposition solution for polarimetric synthetic aperture radar imagery. The Wishart distribution is introduced to the two-component Freeman-Durden (2FD) model to enhance the traditional direct solution (2FD-DS) accuracy. This letter proposes a maximum-likelihood estimator (MLE) (2FD-MLE) expression which is simple enough to numerically solve 2FD unknowns. Furthermore, the statistical randomness impact is observed for the first time. The authors go on to verify that the decomposition results can be greatly improved by MLE, even in a simple physical model. The experiments show that the MLE enhances the estimation accuracy of land-cover types. At a moderate-look scale, the 2FD-MLE has less negative span flaws than the 2FD-DS method, and the estimation results are more close to the physical interpretation.

A new edge detector for polarimetric SAR images based on polarimetric whitening filter aiming at making better use of polarimetric information is proposed. The relativity between different polarization channels including the phase difference and correlation efficient is better utilized in the new algorithm. Therefore, the missing rate is reduced, the continuity of detected edges is enhanced, and the false edge rate is decreased. Experimental results using simulated and real polarimetric SAR data both revealed that the polarimetric information in the SAR data is better utilized in the new algorithm, and the performance for edge detection is better than the typical multichannel ratio of averages algorithms.

Image super-resolution (SR) reconstruction has been a hot research topic in recent years. This technique allows the recovery of a high-resolution (HR) image from several low-resolution (LR) images that are noisy, blurred and down-sampled. Among the available reconstruction frameworks, the maximum a posteriori (MAP) model is widely used. In this model, the regularization parameter plays an important role. If the parameter is too small, the noise will not be effectively restrained; conversely, the reconstruction result will become blurry. Therefore, how to adaptively select the optimal regularization parameter has been widely discussed. In this paper, we propose an adaptive MAP reconstruction method based upon a U-curve. To determine the regularization parameter, a U-curve function is first constructed using the data fidelity term and prior term, and then the left maximum curvature point of the curve is regarded as the optimal parameter. The proposed algorithm is tested on both simulated and actual data. Experimental results show the effectiveness and robustness of this method, both in its visual effects and in quantitative terms.

Image super-resolution reconstruction (SRR) refers to a signal processing approach which produces a high-resolution (HR) image from observed multiple low-resolution (LR) images. In this paper, we propose a joint MAP formulation combining image registration, blur identification, and SRR together to deal with heavy aliasing in the observed LR images. A cyclic coordinate decent optimization procedure is used to solve the formulation, in which the registration parameters, blurring information, and HR image are found in an alternate manner given the others, respectively. The proposed algorithm has been tested on a synthetic image sequence. The experiment results and error analyses verify the efficacy of this algorithm.

Although the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO) has been widely used in aerosol research, the classification of aerosol and cloud still exist some problems. Tradition classification method used by NASA is probability distribution functions (PDFs), but in reality, when we want to realize this algorithm, we fund it is difficult to describe the multi-modal distribution of cloud backscatter coefficients. Further, because ice cloud and dust aerosol have some similar properties, so it is not easy to identify them. In this paper, we introduce a classification method which based on Support vector machine (SVM), and add another characteristic. Then according to the result of classification inverse the aerosol characteristic, the height of cloud top, at the same time, combine with the CloudSat calculate the other cloud character, these data will be helpful for further climate research.

In this paper a new technique of objects measurement based on spectral ratio lidar system has been proposed and developed to make horizontal-path laser measurements of objects. The two or more wavelengths laser transmitter operates within and adjacent to the sensitive bands exhibited by the characteristics of each object, the result could be used to establish inversion models of the laser transmitting backscatter signals. The application value and the key techniques of the spectral lidar are analyzed. The laser spectral ratio model is established and the lidar system is designed, the lidar measurements were down to testify its feasibility. Also issues to approach the final goal of this new technique are discussed.

The main objectives of this research were to select the best hyperspectral narrow bands in the study of rice under different levels of nitrogen and water was used to study the nutritional status of rice. The methodologies employed used partial least squares (PLS) analysis method and spectral bands inter-correlation method (ICM), on the basis of an experiment using reflectance spectra of rice leaves and the concentration of three foliar biochemicals: nitrogen, chlorophyll-a and water, we select the most appropriate wavelengths. We established several broad-band and narrow-band (wavelengths) combinations and compare the inverse effects with each other to determine the inversion accuracy of narrow bands. From the PLS regression result, we confirmed that a 5 narrow bands combination includes 552 nm, 660 nm, 675 nm, 752 nm, 776 nm is the best inversion model of rice leaf nitrogen content, and the 3 narrow bands combination includes 1158 nm, 1378 nm, 1955 nm is much available for rice leaf water content inversion.

In this paper, a new sub-pixel mapping method inspired by the clonal selection algorithm (CSA) in artificial immune systems (AIS) is proposed, namely clonal selection subpixel mapping (CSSM). In CSSM, the sub-pixel mapping problem becomes one of assigning land cover classes to the sub-pixels while maximizing the spatial dependence by clonal selection algorithm. CSSM inherits the biologic properties of human immune systems, i.e. clone, mutation, memory, to build a memory-cell population with a diverse set of local optimal solutions. Based on the memory-cell population, CSSM outputs the value of the memory cell and find the optimal sub-pixel mapping result. The proposed method was tested using the synthetic and degraded real imagery. Experimental results demonstrate that the proposed approach outperform traditional sub-pixel mapping algorithms, and hence provide an effective option for sub-pixel mapping of remote sensing imagery.

In this paper, we propose a super-resolution image reconstruction algorithm to moderate-resolution imaging spectroradiometer (MODIS) remote sensing images. This algorithm consists of two parts: registration and reconstruction. In the registration part, a truncated quadratic cost function is used to exclude the outlier pixels, which strongly deviate from the registration model. Accurate photometric and geometric registration parameters can be obtained simultaneously. In the reconstruction part, the L1 norm data fidelity term is chosen to reduce the effects of inevitable registration error, and a Huber prior is used as regularization to preserve sharp edges in the reconstructed image. In this process, the outliers are excluded again to enhance the robustness of the algorithm. The proposed algorithm has been tested using real MODIS band-4 images, which were captured in different dates. The experimental results and comparative analyses verify the effectiveness of this algorithm.

Studying optical properties of atmospheric aerosol is important because aerosol affects people around the world significantly. These effects strongly depend on the physical and optical properties of aerosol particles. In this paper, we propose to use lidar, sun-photometer, and CALIPSO synchronously, then present combined retrieval to investigate the optical properties of aerosol. The observations were performed at Wuhan during the period of December 2007 to May 2008. The primary results show that the proposed method improved the precision of aerosol optical depth effectively. Furthermore, long-term atmospheric and aerosol data could be obtained by consecutive observations. Also these data will be useful for future understanding about their environmental and climate effects.

The stable aerosol retrieval algorithm needs a prior assumption of lidar ratio (the extinction-to-backscatter ratio), and the known aerosol type that is the prerequisite of this assumption, so how to identify the clouds and aerosol from lidar profile is fundamental to acquire atmospheric optical parameter. In this paper, we first employ the CloudSat to validate the CALISPO's classificatory results, which is released in different versions, after choosing more accurate classification, then start retrieving. Second, sun-photometer is used for verifying the CALIPSO's calibration coefficient and supplies the day time records which are relatively more accurate. Finally, aerosol characteristic in Hubei province is analyzed. All the data will supply more available information for further climate change research.

Obtaining high-accuracy optical property of atmosphere timely will lead to good results of atmospheric correction and real remote sensing image inversion. We have developed a multi-function Raman/Mie lidar system. Making use of this lidar, we can get the spatial distribution and the time evolution of many atmospheric parameters. In this paper, we studied the main factors affecting atmospheric correction, namely absorption and scattering by aerosols and several major atmospheric elements. Preliminary experimental results are described. These data are combined with sunphotometer data and another scanning Mie lidar data, integrated with the imagery from such as remote sensing of the Earth satellite to obtain the ground truth.

in this paper, a new method is proposed, which combine CALIPSO lidar data with AERONET data to acquire the unstable aerosol information in Taiwan. First, introduce a CALIPSO retrieval arithmetic to obtain the aerosol optical depth, and then compare the differences between CALIPSO and AERONET. By combining these two techniques we could not only have the precise site data from AERONET, but also own the change information of aerosol in southeast China from CALIPSO. Different from AERONET, we can also display the spatial properties of aerosol from CALIPSO lidar backscatter data, such as, the strength of aerosol in each layer and their change with time in the aerosphere.

In this paper, a hybrid retrieval method of aerosol optical depth based on the combination of active and passive optical remote sensing is proposed. Two methods to retrieve the atmospheric optical depth are introduced: the so-called dark pixel method is used for retrieving the aerosol optical depth from MODIS; the other one is used by CALIPSO lidar data. After analyzing the two methods, the combined MODIS and CALIPSO method is applied for the aerosol optical depth, the primary experimental results show that these data are in agreement with each other in time-space evolvement trend.

Classification and extraction of spatial features are investigated in urban areas from high spatial resolution multispectral imagery. The proposed approach consists of three steps. First, as an extension of our previous work [pixel shape index (PSI)], a structural feature set (SFS) is proposed to extract the statistical features of the direction-lines histogram. Second, some methods of dimension reduction, including independent component analysis, decision boundary feature extraction, and the similarity-index feature selection, are implemented for the proposed SFS to reduce information redundancy. Third, four classifiers, the maximum-likelihood classifier, backpropagation neural network, probability neural network based on expectation-maximization training, and support vector machine, are compared to assess SFS and other spatial feature sets. We evaluate the proposed approach on two QuickBird datasets, and the results show that the new set of reduced spatial features has better performance than the existing length-width extraction algorithm and PSI

An adaptive multiscale information fusion algorithm is proposed to extract the spatial features and classify IKONOS multispectral imagery. It is well known that combining spectral and spatial information can improve land use classification of very high resolution data. However, many spatial measures refer to the window size problem, and the success of the classification procedure using spatial features depends largely on the window size that was selected. In this letter, we first propose an optimal window selection method, based on the spectral and edge information in a local region, for choosing the suitable window size adaptively; second, the multiscale information is fused based on the selected optimal window size. In order to evaluate the effectiveness of the proposed multiscale feature fusion approach, the spatial features that were extracted by the gray-level cooccurrence matrix are utilized for multispectral IKONOS data. The results show that the proposed algorithm can select and fuse the multiscale features effectively and, at the same time, increase the classification accuracy.

A new stochastic search strategy inspired by the clonal selection theory in an artificial immune system is proposed for dimensionality reduction of hyperspectral remote-sensing imagery. The clonal selection theory is employed to describe the basic features of an immune response to an antigenic stimulus in order to meet the requirement of diversity in the antibody population. In our proposed strategy, dimensionality reduction is formulated as an optimization problem that searches an optimum with less number of features in a feature space. In line with this novel strategy, a feature subset search algorithm, clonal selection Feature-Selection (CSFS) algorithm, and a feature-weighting algorithm, Clonal-Selection Feature-Weighting (CSFW) algorithm, have been developed. In the CSFS, each solution is evolved in binary space, and the value of each bit is either 0 or 1, which indicates that the corresponding feature is either removed or selected, respectively. In CSFW, each antibody is directly represented by a string consisting of integer numbers and their corresponding weights. These algorithms are compared with the following four well-known algorithms: sequential forward selection, sequential forward floating selection, genetic-algorithm-based feature selection, and decision-boundary feature extraction using the hyperspectral remote-sensing imagery acquired by the Pushbroom Hyperspectral Imager and the Airborne Visible/Infrared Imaging Spectrometer, respectively. Experimental results demonstrate that CSFS and CSFW outperform other algorithms and hence provide effective new options for dimensionality reduction of hyperspectral remote-sensing imagery.

Gaussian Markov random fields (GMRFs) are used to analyze textures. GMRFs measure the interdependence of neighboring pixels within a texture to produce features. In this paper, neighboring pixels are taken into account in a priority sequence according to their distance from the center pixel, and a step-by-step least squares method is proposed to extract a novel set of GMRF texture features, named as PS-GMRF. A complete procedure is first designed to classify texture samples of QuickBird imagery. After texture feature extraction, a subset of PS-GMRF features is obtained by the sequential floating forward-selection method. Then, the maximuma posterioriiterated conditional mode classification algorithm is used, involving the selected PS-GMRF texture features in combination with spectral features. The experimental results show that the performance of classifying texture samples on high spatial resolution QuickBird satellite imagery is improved when texture features and spectral features are used jointly, and PS-GMRF features have a higher discrimination power compared to the classical GMRF features, making a notable improvement in classification accuracy from 71.84% to 94.01%. On the other hand, it is found that one of the PS-GMRF texture features - the lowest order variance - is effective for residential-area detection. Some results for IKONOS and SPOT-5 images show that the integration of the lowest order variance with spectral features improves the classification accuracy compared to classification with purely spectral features

The artificial immune network (AIN), which is a new computational intelligence model based on artificial immune systems inspired by the vertebrate immune system, has been widely utilized for pattern recognition and data analysis. However, due to the inherent complexity of current AIN models, their application to remote-sensing image classification has been rather limited. This paper presents a novel supervised classification algorithm based on a multiple-valued immune network, which is a novel AIN model, to perform remote-sensing image classification. The proposed method trains the immune network using the samples of regions of interest and obtains an immune network with memory to classify the remote-sensing imagery. Two experiments with different types of images are performed to evaluate the performance of the proposed algorithm in comparison with other traditional image classification algorithms: Parallelepiped, Minimum Distance, Maximum Likelihood, and Back-Propagation Neural Network. The results evince that the proposed algorithm consistently outperforms the traditional algorithms in all the experiments and, hence, provides an effective option for processing remote-sensing imagery.

Super resolution image reconstruction allows the recovery of a high-resolution (HR) image from several low-resolution images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-resolution problem in which the scenes contain multiple independently moving objects. This formulation is built upon the maximum a posteriori (MAP) framework, which judiciously combines motion estimation, segmentation, and super resolution together. A cyclic coordinate descent optimization procedure is used to solve the MAP formulation, in which the motion fields, segmentation fields, and HR images are found in an alternate manner given the two others, respectively. Specifically, the gradient-based methods are employed to solve the HR image and motion fields, and an iterated conditional mode optimization method to obtain the segmentation fields. The proposed algorithm has been tested using a synthetic image sequence, the "Mobile and Calendar" sequence, and the original "Motorcycle and Car" sequence. The experiment results and error analyses verify the efficacy of this algorithm.

In order to improve signal-to-noise ratio (SNR) and contrast-to-noise ratio, this paper introduces a local variance-controlled forward-and-backward (LVCFAB) diffusion algorithm for edge enhancement and noise reduction. In our algorithm, an alternative FAB diffusion algorithm is proposed. The results for the alternative FAB algorithm show better algorithm behavior than other existing diffusion FAB approaches. Furthermore, two distinct discontinuity measures and the alternative FAB diffusion are incorporated into a LVCFAB diffusion algorithm, where the joint use of the two measures leads to a complementary effect for preserving edge features in digital images. This LVC mechanism adaptively modifies the degree of diffusion at any image location and is dependent on both local gradient and inhomogeneity. Qualitative experiments, based on general digital images and magnetic resonance images, show significant improvements when the LVCFAB diffusion algorithm is used versus the existing anisotropic diffusion and the previous FAB diffusion algorithms for enhancing edge features and improving image contrast. Quantitative analyses, based on peak SNR, confirm the superiority of the proposed LVCFAB diffusion algorithm.

A new atmospheric correction method of earth observation images based on the combination of satellite data and lidar data is proposed in this paper. A mobile scanning Mie lidar was developed to detect the aerosols' spatial and temporal distribution for the purpose above. To obtain more accurate data, future development plan of a multi-wavelength, multi-channel Raman lidar is discussed. Earth observation images processed by the radiative transfer model and this new method are presented. Also issues to approach the final goal of this new atmospheric correction method are discussed.

This paper proposes a novel kernel change detection algorithm (KCD). The input vectors from two images of different times are mapped into a potential much higher dimensional feature space via a nonlinear mapping, which will usually increase the linear margin of change and no-change regions. Then a simple linear distance measure between two high dimensional feature vectors is defined in features space, which corresponds to the complicated nonlinear distance measure in input space. Furthermore the distance measure's dot product is expressed in the combination of kernel functions and large numbers of dot product processed in input space by combined kernel tactic, which avoids the computational load. Finally this paper takes the soft margin single-class support vector machine (SVM) to select the optimal hyper-plane with maximum margin. Preliminary results show the kernel change detection algorithm (KCD) has excellent performance in accuracy.

Shape and spectra are both important features of high spatial resolution remotely sensed (HSRRS) imagery, and they are concrete manifestation of textures on such imagery. This paper presents a spatial feature index, pixel shape index (PSI), to describe the shape feature in a local area surrounding a pixel. PSI is a pixel-based feature which measures the gray similarity distance in every direction. As merely the shape feature is inadequate for classifying HSRRS imagery, a transformed spectral feature extracted by independent component analysis is added to the input vectors of our classifier, and this replaces the original multispectral bands. Meanwhile, a fast fusion algorithm that integrates both shape and spectral features using the support vector machine has been developed to interpret the complex input vectors. The results by PSI are compared with some spatial features extracted using wavelet transform, gray level co-occurrence matrix, and the length-width extraction algorithm to test its effectiveness. The experiments demonstrate that PSI is capable of describing shape features effectively and result in more accurate classifications than other methods. While it is found that spectral and shape features can complement each other and their integration can improve classification accuracy, the transformed spectral components are also found to be more suitable for classification

A new method in computational intelligence namely artificial immune systems (AIS), which draw inspiration from the vertebrate immune system, have strong capabilities of pattern recognition. Even though AIS have been successfully utilized in several fields, few applications have been reported in remote sensing. Modern commercial imaging satellites, owing to their large volume of high-resolution imagery, offer greater opportunities for automated image analysis. Hence, we propose a novel unsupervised machine-learning algorithm namely unsupervised artificial immune classifier (UAIC) to perform remote sensing image classification. In addition to their nonlinear classification properties, UAIC possesses biological properties such as clonal selection, immune network, and immune memory. The implementation of UAIC comprises two steps: initially, the first clustering centers are acquired by randomly choosing from the input remote sensing image. Then, the classification task is carried out. This assigns each pixel to the class that maximizes stimulation between the antigen and the antibody. Subsequently, based on the class, the antibody population is evolved and the memory cell pool is updated by immune algorithms until the stopping criterion is met. The classification results are evaluated by comparing with four known algorithms: K-means, ISODATA, fuzzy K-means, and self-organizing map. It is shown that UAIC is an adaptive clustering algorithm, which outperforms other algorithms in all the three experiments we carried out.

Super-resolution image reconstruction has been one of the most active research areas in recent years. In this paper, a new super-resolution algorithm is proposed to the problem of obtaining a high-resolution image from several low-resolution images that have been sub-sampled and displaced by different amounts of sub-pixel shifts. The algorithm is based on the MAP framework, solving the optimization by proposed iteration steps. At each iteration step, the regularization parameter is updated using the partially reconstructed image solved at the last step. The proposed algorithm is tested on synthetic images, and the reconstructed images are evaluated by the PSNR method. The results indicate that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.