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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 (\textS^4\textC) 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 \textS^4\textC algorithm. [ABSTRACT FROM AUTHOR]