Recently, deep learning-based algorithms have been widely used for classification of hyperspectral images (HSIs) by extracting invariant and abstract features. In our conference paper presented at IEEE International Geoscience and Remote Sensing Symposium 2018, 1-D-capsule network (CapsNet) and 2-D-CapsNet were proposed and validated for HSI feature extraction and classification. To further improve the classification performance, the robust 3-D-CapsNet architecture is proposed in this article by following our previous work, which introduces the maximum correntropy criterion to address the noise and outliers problem, generating a robust and better generalization model. As such, discriminative features can be extracted even if some samples are corrupted more or less. In addition, a novel dual channel framework based on robust CapsNet is further proposed to fuse the hyperspectral data and light detection and ranging-derived elevation data for classification. Three widely used hyperspectral datasets are employed to demonstrate the superiority of our proposed deep learning models.

Recently, sparse and low-rank graph-based discriminant analysis (SLGDA) has yielded satisfactory results in hyperspectral image (HSI) dimensionality reduction (DR), for which sparsity and low-rankness are simultaneously imposed to capture both local and global structure of hyperspectral data. However, SLGDA fails to exploit the spatial information. To address this problem, a tensor sparse and low-rank graph-based discriminant analysis (TSLGDA) is proposed in this paper. By regarding the hyperspectral data cube as a third-order tensor, small local patches centered at the training samples are extracted for the TSLGDA framework to maintain the structural information, resulting in a more discriminative graph. Subsequently, dimensionality reduction is performed on the tensorial training and testing samples to reduce data redundancy. Experimental results of three real-world hyperspectral datasets demonstrate that the proposed TSLGDA algorithm greatly improves the classification performance in the low-dimensional space when compared to state-of-the-art DR methods.

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 article, we propose a multifrequency graph convolutional network with cross-modality mutual enhancement (MFGCN-CME) for multisource RS data classification. Specifically, we design an adaptive multifrequency 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 (BG) enhancement learning module to obtain the spatial-enhanced HSI features and spectral-enhanced LiDAR features by propagating intermodality information. To the best of our knowledge, the BG 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 semisupervised 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.

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.

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.