Recently, Vision Transformer (ViT)-based deep learning models have achieved remarkable performance gains in hyperspectral image classification (HSIC) due to their abilities to model long-range dependencies and extract global spatial features. However, ViT is built with a stack of Transformer blocks and faces the challenge of learning a large number of parameters when processing hyperspectral data. Besides, the inherent modeling of global correlation in Transformer ignores the effective representation of local spatial and spectral features. To address these issues, we propose a lightweight ViT network known as Groupwise Separable Convolutional Vision Transformer (GSC-ViT). Firstly, a Groupwise Separable Convolution (GSC) module, which is a combination of grouped pointwise convolution and group convolution, is designed to significantly decrease the number of convolutional kernel parameters, and effectively capture local spectral-spatial information in hyperspectral image. Secondly, a Groupwise Separable Multi-Head Self-Attention (GSSA) module is employed to substitute the conventional Multi-Head Self-Attention (MSA) in ViT, in which the Groupwise Self-Attention(GSA) provides local spatial feature extraction, and the Pointwise Self-Attention(PWSA) provides global spatial feature extraction. Thirdly, a simple pointwise layer with enhanced skip connection mechanism is employed to substitute the Multi-Layer Perceptron (MLP) layer in all Transformer blocks of ViT, so as to eliminate unnecessary nonlinear transformations and facilitate the fusion of features derived from GSC and GSSA modules. Extensive experiments on four benchmark hyperspectral datasets reveal that our GSC-ViT can achieve surprising classification performance with relatively few training samples as compared with some existing HSIC approaches. The source code is available at https://github.com/flyzzie/TGRS-GSC-VIT.

Hyperspectral unmixing is an important problem for remotely sensed data interpretation. It amounts at estimating the spectral signatures of the pure spectral constituents in the scene (endmembers) and their corresponding subpixel fractional abundances. Although the unmixing problem is inherently nonlinear (due to multiple scattering), the nonlinear unmixing of hyperspectral data has been a very challenging problem. This is because nonlinear models require detailed knowledge about the physical interactions between the sunlight scattered by multiple materials. In turn, bilinear mixture models (BMMs) can reach good accuracy with a relatively simple model for scattering. In this article, we develop a new BMM and a corresponding unsupervised unmixing approach which consists of two main steps. In the first step, a deep autoencoder is used to linearly estimate the endmember signatures and their associated abundance fractions. The second step refines the initial (linear) estimates using a bilinear model, in which another deep autoencoder (with a low-rank assumption) is adapted to model second-order scattering interactions. It should be noted that in our developed BMM model, the two deep autoencoders are trained in a mutually interdependent manner under the multitask learning framework, and the relative reconstruction error is used as the stopping criterion. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data sets. Our experimental results indicate that the proposed approach can reasonably estimate the nature of nonlinear interactions in real scenarios. Compared with other state-of-the-art unmixing algorithms, the proposed approach demonstrates very competitive performance.

In recent years, deep learning (DL) has accelerated the development of hyperspectral image (HSI) processing, expanding the range of applications further. As a typical model of unsupervised DL, the autoencoder framework has been extensively applied for spectral unmixing due to its strong representation ability and scalability. Nowadays, most DL-based unmixing approaches adopt the linear mixture model (LMM) to estimate pure spectral signatures (endmembers) and their corresponding abundance fractions. However, since sunlight scattering is an inevitable physical phenomenon, the spectral mixture problem is inherently nonlinear. Moreover, most existing nonlinear unmixing approaches focus exclusively on spectral information, neglecting the spatial distribution of materials and the intrinsic correlation between pixels, making it challenging to explore latent features. To address these issues, this paper develops a new deep autoencoder-based augmented network (DAAN). The proposed DAAN employs the multilinear mixture model (MLMM) to handle the nonlinear influence caused by multiple scattering. Meanwhile, the proposed DAAN constraints homogenous smoothing in the autoencoder architecture, enabling the aggregation of intrinsic correlations by means of spatial relationships to enhance the performance of abundance estimation. We achieve unsupervised nonlinear hyperspectral unmixing by combining spectral and spatial information. The effectiveness and advantages of DAAN are confirmed by several experiments with synthetic and real HSI datasets. The results indicate that the proposed method outperforms other DL-based unmixing approaches. The source codes of the proposed DAAN will be provided in the following link https://github.com/yuanchaosu/TGRS-daan.

Deep learning (DL) has brought many new trends for hyperspectral image classification (HIC). Graph neural networks (GNNs) are models that fuse DL and structured data. Although GNN-based methods have focused on modeling relations, most of them are susceptible to noise, being adverse to capturing hidden correlations from data. Moreover, existing related approaches typically adopt changeless graph structures, which might lead to poor generalization. To solve the problems mentioned above, this paper develops an adaptive cuckoo refinement-based graph transfer network (ACGT-Net) that introduces a meta-heuristic optimization strategy to refine the graph structure. Specifically, we first pre-train a graph convolutional network (GCN) to learn transferable weight parameters. In the undirected graph, nodes are associated with pixels, and edges correspond to similarities between nodes. Afterward, we integrate a cuckoo search strategy (CSS) into the trained GCN to adaptively refine the graph structure. The graph structure refinement (GSR) with the CSS can pay more attention to significant channels by global optimization to improve the generalization of the GNN. Several experiments with real datasets verify the effectiveness and competitiveness of our ACGT-Net compared with other state-of-the-art (SOTA) methods.

Region of interest (ROI) localization is one of the key preprocessing technologies for a finger-vein identification system, so an effective ROI definition can improve the matching accuracy. However, due to the impact of uneven illumination, equipment noise, as well as the distortion of finger position, etc., these make accurate ROI localization a very difficult task. To address these issues, in this paper, we propose a robust finger-vein ROI localization method, which is based on the 3 $\sigma$ criterion dynamic threshold strategy. The proposed method includes three main steps: First, the Kirsch edge detector is introduced to detect the horizontal-like edges in the acquired finger-vein image. Then, the obtained edge gradient image is divided into four parts: upper-left, upper-right, lower-left, and lower-right. For each part of the image, the three-level dynamic threshold, which is based on the 3 $\sigma$ criterion of the normal distribution, is imposed to obtain more distinct and complete edge information. Finally, through labeling the longest connected component at the same horizontal line, two reliable finger boundaries, which represent the upper and lower boundaries, respectively, are defined, and the ROI is localized in the region between these two boundaries. Extensive experiments are carried out on four different finger-vein image datasets, including three publicly available datasets and one of our newly developed finger-vein datasets with 37,080 finger-vein samples and 1030 individuals. The experimental results indicate that our proposed method has very competitive ROI localization performance, as well as satisfactory matching results on different datasets.