Infrared small target detection (IRSTD) has progressed significantly in spatial-domain learning. However, single-frame images’ limited spatial semantics impair discrimination between targets and similar noise while complicating integrity detection of large-scale target. To address these, we propose Global Spatial-Frequency Attention Network (GSFANet), which enhances the distribution difference between targets and noise from a frequency-domain perspective while preserving spatial information integrity. The core innovations consist of three modules: 1) Parametric Wavelet Downsampling (PWD), preserving small target details during frequency refinement to prevent feature fragmentation; 2) Hierarchical Gated Kernel Attention (HGKA), capturing cross-level frequency relationships through Cross-channel Kernel Attention (C2K) and maintaining spatial coherence via Cross-spatial Gate Attention (CSG), effectively bridging semantic gaps across layers; 3) Adaptive Frequency-Decoupled Fusion (AdaFD), dynamically fusing target-associated frequency components while suppressing noise. We further develop AdaFL Loss to balance multi-scale target gradients and stabilize training. Experiments on three benchmark datasets demonstrate GSFANet’s superior detection performance and enhanced segmentation robustness in complex scenarios compared to state-of-the-art methods. Our code will be made public at https://github.com/dengfa02/GSFANet_IRSTD.

Existing hyperspectral image classification (HSIC) models based on the Mamba architecture predominantly center on characterizing the original spectral and spatial domains, with limited exploration of time–frequency analysis. In this study, we propose a novel wavelet decomposition-based spectral–spatial Mamba network for HSIC, dubbed “WD-SSMamba.” This model incorporates both 1-D and 2-D wavelet decompositions to extract spectral and spatial features in the frequency domain, respectively. Specifically, we design an innovative frequency feature extraction (FE) block, which comprises a spectral wavelet convolution (SWC) module for spectral FE and a wavelet separable convolution (WSC) module for spatial FE. Furthermore, to address the challenges of integrating spectral–spatial features in traditional Mamba models for HSIC, we devise a dual-branch Mamba block featuring a cross-fusion structure, termed the “HyperMamba” block, to efficiently extract and fuse spectral and spatial features. Comprehensive experiments were conducted on four publicly available hyperspectral image (HSI) datasets, namely, Pavia University, WHU-Hi-LongKou, WHU-Hi-HongHu, and Houston 2013. The results show that the WD-SSMamba model achieves overall accuracies (OAs) of 92.48%, 97.91%, 89.98%, and 87.00% on these datasets, respectively, with fewer than 20 training samples per class, surpassing those of other competing models across all tested datasets. Moreover, it significantly reduces the number of parameters to less than 50k and floating-point operations (FLOPs) to less than 3.02M, thereby fully showcasing the immense potential of frequency analysis and the Mamba structure in HSIC.

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 article 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 linkhttps://github.com/yuanchaosu/TGRS-daan.

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.

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.

Nowadays, convolutional neural network (CNN)-based deep learning models have been popularized in hyperspectral image classification (HSIC) and achieved significant accuracy gains, which is due to their hierarchical and nonlinear feature learning patterns. However, too deeper network structures may induce a huge amount of parameters and excessive computing overhead, leading to the need for plenty of labeled samples for training. Besides, highly abstract semantic features may not be the most suitable for hyperspectral land-cover classification tasks. To address these issues, we propose a fairly lightweight network model for HSIC, which is built on a type of exquisitely designed convolution module, namelygrouped separable convolution. Compared with the standard convolution, the designed grouped separable convolution module combines grouped convolution with point-wise convolution, which not only greatly reduces the number of parameters of convolution kernels, but also caters to the inherent 3D cube style of hyperspectral image data. Moreover, Gabor filters are introduced to modulate the grouped separable convolution kernels, so as to further use relatively few convolution kernels with additional prior orientation and scale information for feature extraction. The experiments are carried out on four real hyperspectral datasets, and the experimental results reveal that the proposed model has low training cost and memory overhead. Compared with some existing deep network models that have been applied to HSIC, our proposed model can achieve competitive classification accuracy with fewer training samples.

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.

Hyperspectral unmixing (HU) is an active research topic in the remote-sensing community. It aims at modeling mixed pixels using a collection of pure constituent materials (endmembers) weighted by their corresponding fractional abundances. Among existing unmixing schemes, nonnegative matrix factorization (NMF) has drawn significant attention due to its unsupervised nature, as well as its capacity to obtain both endmembers and fractional abundances simultaneously. In this article, we present a new blind unmixing method based on the generalized morphological component analysis (GMCA) framework, in which an additional constraint is introduced into the standard NMF model to represent the sparsity and morphological diversity of the abundance maps associated with each endmember. More specifically, we take into account the fact that different ground categories in a hyperspectral scene generally exhibit various spatial distributions and morphological characteristics. As a result, when providing a specific dictionary basis for these categories, their corresponding abundance maps (referred to as sources) can be sparsely represented. In addition, due to the low correlation between different sources, their sparse representations will not share the same most significant coefficients. With this observation in mind, we can further promote source discrimination and separation in the unmixing process. Moreover, in order to obtain a stable solution of the involved optimization problem, we adopt an alternate iterative constrained algorithm with a threshold descent strategy. Our experiments, carried out on both synthetic and real hyperspectral scenes, reveal that our newly developed GMCA-based unmixing method obtains very promising results with fast convergence speed and requiring significantly less parameter tuning. This confirms the advantage of the proposed spatial morphological component approach for HU purposes.

Land-cover classification with hyperspectral imagery has been an active topic in the remote sensing community. It aims at relating a unique class label to each pixel in the scene, so that it can be well defined by a given land cover type. In this paper, we explore the intrinsic characteristics of hyperspectral imagery from a subpixel-level perspective and propose a new subpixel component analysis (SCA) approach for feature extraction and land-cover classification. The core idea of SCA is that we extract a subpixel attribute component feature from the abundance maps. Compared with the abundance maps, the extracted subpixel feature image shows higher signal-to-noise level and clearer spatial distribution details. In order to deal with spectral variability, as well as obtain representative image endmember signatures and their corresponding abundance maps, we adopt a regional clustering-based spatial preprocessing (RCSPP) strategy for endmember identification, and a partial unmixing model based on mixture tuned matched filtering (MTMF) for abundance estimation. Furthermore, to highlight the spatial distribution details as well as eliminate the noise disturbance in the derived abundance maps, we perform sparse image decomposition on the obtained abundance maps, thus achieving a new subpixel feature representation for classification. Our experimental results reveal that the proposed SCA approach can obtain feature representation with explicit physical meaning, clear spatial distribution details, and better noise robustness, leading to state-of-the-art classification results.

This paper presents a subpixel spatial-spectral feature mining approach for hyperspectral image classification. First, a regional clustering-based spatial preprocessing (RCSPP) strategy is introduced to identify the endmember signatures from the original image. Then, a partial unmixing model of mixture tuned matched filtering (MTMF) is adopted to estimate the abundance maps. Finally, the morphological component analysis (MCA) is adopted to decompose the abundance map into different spatial morphological components, and the smoothness components are chosen for classification. The experimental results reveal that the obtained subpixel spatial-spectral feature can lead to very good classification accuracies.

In remote sensing image classification, active learning aims to learn a good classifier as best as possible by choosing the most valuable (informative and representative) training samples. Multiview is a concept that regards analyzing the same object from multiple different views. Generally, these views show diversity and complementarity of features. In this paper, we propose a new multiview active learning (MVAL) framework for hyperspectral image classification. First, we generate multiple views by extracting different attribute components from the same image data. Specifically, we adopt the multiple morphological component analysis to decompose the original image into multiple pairs of attribute components, including content, coarseness, contrast, and directionality, and the smooth component from each pair is chosen as one single view. Second, we construct two multiview intensity-based query strategies for active learning. On the one hand, we exploit the intensity differences of multiple views along with the samples' uncertainty to choose the most informative candidates. On the other hand, we consider the clustering distribution of all unlabeled samples, and query the most representative candidates in addition to the highly informative ones. Our experiments are performed on four benchmark hyperspectral image data sets. The obtained results show that the proposed MVAL framework can lead to better classification performance than the traditional, single-view active learning schemes. In addition, compared with the conventional disagree-based MVAL scheme, the proposed query selection strategies show competitive classification accuracy.

This paper presents a new classification framework for the fusion of hyperspectral and LiDAR data. The proposed approach aims at exploiting the complementarity of the features, i.e., textural features in the hyperspectral data and the height features in the LiDAR data, respectively. In this work, we use a morphological component analysis (MCA) method for textural feature extraction. The classification is then executed by a multinomial logistic regression classifier (MLR). Our obtained experimental results reveal that the proposed feature fusion method can lead to very good classification results.

Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing images. The proposed MMCA framework separates a given image into multiple pairs of morphological components (MCs) based on different textural features, with the ultimate goal of improving the signal-to-noise level and the data separability. A distinguishing feature of our proposed approach is the possibility to retrieve detailed image texture information, rather than using a single spatial characteristic of the texture. In this paper, four textural features: content, coarseness, contrast, and directionality (including horizontal and vertical), are considered for generating the MCs. In order to evaluate the obtained MCs, we conduct classification by using both remotely sensed hyperspectral and polarimetric synthetic aperture radar (SAR) scenes, showing the capacity of the proposed method to deal with different kinds of remotely sensed images. The obtained results indicate that the proposed MMCA framework can lead to very good classification performances in different analysis scenarios with limited training samples.

In this work, we propose a new multiple morphological component analysis (MMCA) based decomposition framework for remote sensing image classification. The proposed MMCA framework aims at exploiting relevant textural characteristics present in a scene such as content, coarseness, contrast or directionality. Specifically, MMCA decomposes an image into a pair of morphological components (for each textural characteristic), which can be associated to a smooth and a textural components. The extracted features are then used for classification with a multinomial logistic regression (MLR). The experimental results, conducted using both a hyperspectral and a synthetic aperture radar (SAR) images, reveal that the proposed scheme can lead to state-of-the-art classification accuracy.