The mining of meaningful features and effective fusion of multisource remote sensing (RS) data have always been the challenging research problems in the joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. In this paper, we propose a Multi-Frequency Graph Convolutional Network with Cross-modality Mutual Enhancement (MFGCN-CME) for multisource RS data classification. Specifically, we design an adaptive multi-frequency graph feature learning module to capture the low- and high-frequency multiscale features of HSI and LiDAR in parallel and further adaptively aggregate them. Then, we propose a bipartite graph enhancement learning module to obtain the spatial-enhanced HSI features and spectral-enhanced LiDAR features by propagating inter-modality information. To the best of our knowledge, the bipartite graph is first used to multisource RS data classification task. Furthermore, compared with traditional fusion methods, a gated fusion module is used to fully explore the complementarity of two data sources. Finally, a joint loss function combing a classification loss and a semi-supervised contrastive loss is developed to improve the model robustness. Comprehensive experiments on different HSI and LiDAR datasets demonstrate that our proposed method can yield better performance compared with several state-of-the-art multisource RS data classification methods.

The Wishart mixture model (WMM) has been widely used for classification of polarimetric synthetic aperture radar (PolSAR) images; however, the WMM-based models usually fail to provide reliable classification results and explore the spatial information effectively in the heterogeneous areas. As such, an unsupervised spatially variant Gamma-WMM with extended variational inference algorithm (SVGaWMM-EVI) is proposed for classification of PolSAR images. Firstly, the Gamma prior distribution is imposed on the texture variable of the proposed model, which associates a set of unique texture variables with each data point to utilize the spatial information in the heterogeneous areas. Then, since the existing expectation maximization-based WMM algorithms usually fall into local optimal and update slowly, an extended variational inference algorithm is developed to improve the parameter estimation of our model, where a help function is designed to solve the intractable term. Experimental results on the real-world PolSAR data set demonstrate that our model can obtain better performance than some widely used unsupervised methods.

Owing to the strong correlation between the spectral bands of hyperspectral images (HSIs), many feature extraction (FE) methods have been proposed to reduce the redundancy of hyperspectral data. However, Euclidean distance-based FE methods are sensitive to noise. To address this issue, this letter proposed a new unsupervised FE method called robust projection learning (RPL) by integrating the low-rank and sparse decomposition with projection learning. Specifically, in order to enhance the discrimination of traditional robust principal component analysis (RPCA), discriminative RPCA (DRPCA) is first proposed by decomposing the raw data into a low-rank part, a discriminative sparse part, and a structured noise. Moreover, for the purpose of redundancy reduction, projection learning is integrated into DRPCA to obtain a projection matrix with robustness and discrimination. To verify the validity of RPL, two real hyperspectral data sets are used for basic comparison and robust analysis. The corresponding experimental results demonstrate that RPL outperforms the comparative FE methods.

Recently, graph convolutional networks (GCNs) have been developed to explore the spatial relationship between pixels, achieving better classification performance of hyperspectral images (HSIs). However, these methods fail to sufficiently leverage the relationship between spectral bands in HSI data. As such, we propose an adaptive cross-attention-driven spatial–spectral graph convolutional network (ACSS-GCN), which is composed of a spatial GCN (Sa-GCN) subnetwork, a spectral GCN (Se-GCN) subnetwork, and a graph cross-attention fusion module (GCAFM). Specifically, Sa-GCN and Se-GCN are proposed to extract the spatial and spectral features by modeling the correlations between spatial pixels and between spectral bands, respectively. Then, by integrating attention mechanism into information aggregation of the graph, the GCAFM, including three parts, i.e., the spatial graph attention block, the spectral graph attention block, and the fusion block, is designed to fuse the spatial and spectral features, and suppress noise interference in Sa-GCN and Se-GCN. Moreover, the idea of the adaptive graph is introduced to explore an optimal graph through backpropagation during the training process. Experiments on two HSI datasets show that the proposed method achieves better performance than other classification methods.

In recent years, deep learning has presented a great advance in the hyperspectral image (HSI) classification. Particularly, long short-term memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs. However, the loss of spatial information makes it quite difficult to obtain better performance. In order to address this problem, two novel deep models are proposed to extract more discriminative spatial-spectral features by exploiting the convolutional LSTM (ConvLSTM). By taking the data patch in a local sliding window as the input of each memory cell band by band, the 2-D extended architecture of LSTM is considered for building the spatial-spectral ConvLSTM 2-D neural network (SSCL2DNN) to model long-range dependencies in the spectral domain. To better preserve the intrinsic structure information of the hyperspectral data, the spatial-spectral ConvLSTM 3-D neural network (SSCL3DNN) is proposed by extending LSTM to the 3-D version for further improving the classification performance. The experiments, conducted on three commonly used HSI data sets, demonstrate that the proposed deep models have certain competitive advantages and can provide better classification performance than the other state-of-the-art approaches.

In this paper, we propose two novel classification frameworks for hyperspectral image (HSI) based on capsule network (CapsNet), which could address the drawbacks of convolutional neural network (CNN) and problem of limited training samples by introducing affine transformation matrix. Specifically, the proposed framework first performs the classification of HSI based on spectral information. Second, considering the importance of spatial information for HSI processing, we integrate the spatial and spectral information into the proposed framework to further improve the classification performance. Experimental results on real HSI data demonstrate the effectiveness of the proposed framework.

By considering the cubic nature of hyperspectral image (HSI) to address the issue of the curse of dimensionality, we have introduced a tensor locality preserving projection (TLPP) algorithm for HSI dimensionality reduction and classification. The TLPP algorithm reveals the local structure of the original data through constructing an adjacency graph. However, the hyperspectral data are often susceptible to noise, which may lead to inaccurate graph construction. To resolve this issue, we propose a modified TLPP (MTLPP) via building an adjacency graph on a dual feature space rather than the original space. To this end, the region covariance descriptor is exploited to characterize a region of interest around each hyperspectral pixel. The resulting covariances are the symmetric positive definite matrices lying on a Riemannian manifold such that the Log-Euclidean metric is utilized as the similarity measure for the search of the nearest neighbors. Since the defined covariance feature is more robust against noise, the constructed graph can preserve the intrinsic geometric structure of data and enhance the discriminative ability of features in the low-dimensional space. The experimental results on two real HSI data sets validate the effectiveness of our proposed MTLPP method.

To effectively reduce the spectral variation that degrades classification performance, a novel low-rank subspace recovery method based on latent low-rank representation (LatLRR) is proposed for hyperspectral images in this letter. Different from the robust principal component analysis, LatLRR focuses on exploring the low-rank property from the perspective of row space and column space simultaneously through the low-rank regularization on their corresponding coefficient matrix. Following that, the self-expressiveness-based reconstruction is adopted to recover the intrinsic data from row and column spaces. More accurate subspace structure can be successfully preserved both in spectral domain and spatial domain; meanwhile, the robustness to noise is improved. Experimental results on two hyperspectral data sets demonstrate the effectiveness of the proposed method.

By considering the cubic nature of hyperspectral image (HSI) and to address the issue of the curse of dimensionality, we introduce a tensor locality preserving projection (TLPP) algorithm for HSI classification. TLPP has been proved to be effective in preserving the geometrical structure of data for dimensionality reduction. More importantly, data can be taken directly in the form of a tensor of arbitrary order as input, such that the damage to sample's geometrical structure is avoided during vectorizing. For the HSI classification, TLPP can effectively embed both spatial structure and spectral information into low-dimensional space simultaneously by a series of projection matrices trained for each mode of input samples. The experimental results on the AVIRIS hyperspectral image confirm the effectiveness of TLPP.

In this letter, a low-rank and sparse representation classifier with a spectral consistency constraint (LRSRC-SCC) is proposed. Different from the SRC that represents samples individually, LRSRC-SCC reconstructs samples jointly and is able to capture the local and global structures simultaneously. In this proposed classifier, an adaptive spectral constraint is imposed on both the low-rank and sparse terms so as to better reveal the data structure and enhance its discriminative power. In addition, the alternating direction method is introduced to solve the underlying minimization problem, in which, more importantly, the subobjective function associated with the low-rank term is optimized based on the rank equivalence between a matrix and its Gram matrix, resulting in a closed-form solution. Finally, LRSRC-SCC is extended to LRSRC-SCCE for fully exploiting the spatial information. Experimental results on two hyperspectral data sets demonstrate that the proposed LRSRC-SCC and LRSRC-SCCE methods outperform some state-of-the-art methods.

Due to the high-dimensional characteristic of hyperspectral images, dimensionality reduction (DR) is an important preprocessing step for classification. Recently, sparse and low-rank graph-based discriminant analysis (SLGDA) has been developed for DR of hyperspectral images, for which the properties of sparsity and low-rankness are simultaneously exploited to capture both local and global structures. However, SLGDA may not achieve satisfactory results when handling complex data with nonlinear nature. To address this problem, this paper presents two kernel extensions of SLGDA. In the first proposed classical kernel SLGDA (cKSLGDA), the kernel trick is exploited to implicitly map the original data into a high-dimensional space. With a totally different perspective, we further propose a Nyström-based kernel SLGDA (nKSLGDA) by constructing a virtual kernel space by the Nyström method, in which virtual samples can be explicitly obtained from the original data. Both cKSLGDA and nKSLGDA can achieve more informative graphs than SLGDA, and offer superiority over other state-of-the-art DR methods. More importantly, the nKSLGDA can outperform cKSLGDA with much lower computational cost.

This paper addresses the problem of hyperspectral image classification with the low-rank representation (LRR) which has been widely applied in computer vision and pattern recognition. As is known, it has been proved to be effective in subspace segmentation under the assumption that all the subspaces are mutually independent. Nevertheless, in practical applications, this assumption could hardly be guaranteed. In this paper, to sidestep this limitation, we simultaneously exploit the spectral similarity and spatial information of pixels to design a local constraint as the regularizer of LRR, which is referred to as the locality constrained LRR (LCLRR). The experimental results on the AVIRIS hyperspectral image confirm the effectiveness of our proposed method.