Recently, the selective structured state space model (S6) built upon the Mamba architecture has attracted widespread attention for its outstanding performance in long-range modeling. However, existing Mamba-based hyperspectral image (HSI) classification methods suffer from certain limitations in extracting edge information. To address this problem, a new HSI classification framework called Pinwheel and Wavelet-based Spatial-Spectral Mamba (S 2 PW-Mamba) is proposed in this paper. The input to our S 2 PW-Mamba is the complete, unsegmented image, which preserves the correlation between pixels in the HSI while leveraging convolution to enhance the extraction of local information. Specifically, two S6 modules are employed along different scanning directions to extract the spatial-spectral features from the HSI, acting on the spectral and spatial domains, respectively. Meanwhile, a GateFusion module is designed to adaptively guide the fusion of spatial and spectral features by discerning their relative importance. More precisely, our S 2 PW-Mamba first extracts both edge and central spatial features using a Pinwheel-shaped convolution and a four-directional spatial scanning module, effectively capturing spatial-contextual relationships. Subsequently, by integrating the wavelet transform with the S6 architecture, the model is capable of capturing both global contextual dependencies and local texture details within the spectral domain. Comprehensive experiments on three benchmark HSI datasets show that our approach outperforms existing state-of-the-art methods.

Cross-scene hyperspectral image classification aims to identify a new scene in target domain via learned knowledge from source domain using limited training samples. Existing cross-scene alignment approaches focus on aligning the global feature distribution between the source and target domains while overlooking the fine-grained alignment at different levels. Moreover, they mainly use Transformer architectures to model long-range dependencies across different channels but confront efficiency challenges due to their quadratic complexity, which limits classification performance in unsupervised domain adaptation tasks. To address these issues, a new domain-adaptive Mamba (DAMamba) is proposed for cross-scene hyperspectral image classification. First, a spectral-spatial Mamba is developed to extract high-order semantic features from the input data. Then, a domain-invariant prototype alignment method is proposed from three perspectives, i.e., intra-domain, inter-domain, and mini-batch, to produce reliable pseudo-labels and mitigate the spectral shift between the source and target domains. Finally, a fully connected layer is applied to the aligned features in the target domain to obtain the final classification results. Extensive evaluations across diverse cross-scene datasets demonstrate that our DAMamba outperforms existing state-of-the-art methods in classification accuracy and computing time. The code of this paper is available at https://github.com/PuhongDuan/DAMamba.

Processing high-dimensional data cubes and developing high-performance classifiers are core objectives in the field of hyperspectral image classification (HSIC). Superpixel-based methods are widely used in HSIC due to their efficacy in reducing redundant information and enhancing local features. However, imprecise segmentation, especially in complex structures and textures of hyperspectral images (HSIs), may lead to inconsistencies in the regions extracted by superpixels and the boundaries between different ground objects. Such inconsistencies significantly degrade the classification performance of HSIs. Alternatively, when parameter settings are inaccurate, edge-aware feature extraction methods often introduce sharpening artifacts at the image boundaries, resulting in a decrease in classification accuracy. To effectively address these challenges, we propose a novel probabilistic fusion method for HSIC. This method consists of the following stages. First, spatial information is extracted by a multiscale superpixel segmentation method and then probabilistically optimized by the extended random walk (ERW) method. Next, semantic-aware structural features (S2Fs) are extracted along with edge information of different objects. Lastly, a probabilistic framework is proposed to fuse the class probabilities of superpixel-based spatial information and semantic-aware structural features. Experimental results on three real datasets show state-of-the-art classification performance, even with limited training sets.

Recently, Convolutional Neural Networks (CNNs) and Transformer have achieved considerable success in Hyperspectral Image (HSI) classification tasks. However, existing methods not only lack the study of spectral variability of samples from the same land class, but also struggle to mine local-global spectral information and spatial structure information of HSI at different granularities effectively. To mitigate these limitations, this article proposes a multigranularity spatial-spectral convolution Transformer network (MSSCFormer), which can reduce the intra-class spectral differences of samples and extract multigranularity spatial-spectral features from a local-global-local perspective. Specifically, MSSCFormer consists of three components: intra-class spectral attention (ICSA), spatial-spectral feature extractor (SSFE), and global-local convolutional Transformer (GLCT). Firstly, ICSA redistributes the spectral weights of samples of the same land class by establishing an attention mapping between spectral channels within the class to reduce the intra-class spectral differences. Second, SSFE extracts shallow spatial features and multigranularity spectral features of HSI with reassigned spectral weights samples from a local perspective. Finally, GLCT takes advantage of CNNs and Transformer, it uses MHSA to model global spectral features and utilizes local context feature block (LCFB) to capture local spatial features in a multigranularity way. Experimental results on three benchmark datasets show that MSSCFormer exhibits excellent performance on the HSI datasets and outperforms state-of-the-art HSI classification algorithms.

Convolutional Neural Networks (CNNs) and Transformers have made remarkable achievements in hyperspectral image classification (HSIC). Unfortunately, CNN-based methods struggle to capture the contextual dependencies between pixels in HSIs, while Transformer-based methods suffer from quadratic computational complexity. Recently, the Mamba model has shown great potential as it can describe long-range dependencies between HSI pixels with linear computational complexity. Yet, Mamba still faces significant challenges in terms of global modeling. Inspired by the Mamba model framework and Transformers, a novel Dual-Branch Mamba-Like Linear Attention (DBMLLA) network is proposed for HSIC, achieving efficient global dependency modeling. Specifically, the proposed DBMLLA combines an embedding module, a Spatial-Spectral Mamba-Like Linear Attention (SS-MLLA) module, and a fusion module. In the embedding module, an absolute position embedding module is introduced for better extraction of global features. In the SS-MLLA module, we design the Spatial Mamba-Like Linear Attention (SpaMLLA) block and the Spectral Mamba-Like Linear Attention (SpeMLLA) block to exploit the spatial and spectral information of the HSI. In addition, SS-MLLA is improved by utilising Depthwise Separable Convolution (DSC) to enhance the model’s ability to extract deeper local feature information. Through experiments conducted on four public hyperspectral datasets, it is demonstrated that the proposed model consistently outperforms state-of-the-art approaches.

Transformer models have achieved remarkable success in hyperspectral image classification (HSIC) owing to their strong global modeling capability. However, their quadratic complexity significantly limits their computational efficiency. Recently, Mamba has been applied to HSIC because of its linear complexity, yet it still suffers from an imbalance between global and local modeling. To overcome these challenges, this paper proposes a novel Learnable Kernel and Mamba with Spatial-Spectral Attention Fusion (LKMA) framework, which enables the extraction of global-local spatial-spectral features (SSF) while enhancing edge feature representation. For local feature extraction, the proposed Multi-Scale Spatial-Spectral Feature Generation (MSSFG) module captures local SSF by employing multi-scale learnable dilation convolutions for spatial features and multi-scale dilation convolutions for spectral features. For global feature extraction, a Global Hidden Mixing Mamba (GHMM) module is introduced, which projects hyperspectral image (HSI) features from the feature space to the hidden state space via a hidden state mixing mechanism. This enables the model to capture contextual semantic information and local details from the HSI. To further explore the synergistic effect between spatial and spectral information, the Spatial-Spectral Attention Fusion (SSAF) module integrates semantic information across multiple feature groups by combining Semantic Grouped Spatial Attention (SGSA) and Progressive Spectral Self-Attention (PSSA), enhancing spatial-spectral representations. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art approaches for HSIC.

Deep learning-based methods for hyperspectral image classification (HSIC) have been widely utilized in recent years. However, existing HSIC methods do not adequately account for illumination variations in HSIs, particularly in urban areas where shadows created by complex ground objects result in significant variations that cannot be ignored. Additionally, when dealing with limited labeled data, most deep learning methods are prone to overfitting, resulting in poor classification performance. To address these challenges, we propose a new Retinex-based dual-branch feature extraction network (RDFEN) for HSIC. First, by incorporating Retinex theory, we propose a hyperspectral Retinex (HyperRetinex) module to extract illumination attributes and reflectance attributes. Then, we propose a dual-branch feature extraction network, which consists of two submodules: illumination attributes feature extraction (IAFE) module and reflectance attributes feature extraction (RAFE) module. Finally, an illumination-reflectance attributes interaction attention fusion (IRAIAF) module is strategically designed to integrate distinct features. Experiments on four benchmark HSI datasets demonstrate that the proposed method outperforms other state-of-the-art HSIC methods, achieving up to 96.85% overall accuracy on the Pavia University dataset, thereby highlighting its effectiveness and robustness in HSIC. For reproducibility, the code is available at https://github.com/JT-shen/Code.

Feature extraction is a prevalent technique in hyperspectral remote sensing. Various tasks require this technique as a pre-processing step, including image classification, anomaly detection, image denoising, and so on. Edge-preserving filtering based methods have been extensively utilized for this purpose. However, these methods do not take the inherent structural and textural information into account, leading to poor performance in classifying hyperspectral images (HSIs). In this letter, a new structural and textural-aware feature extraction method is proposed that preserves the relevant structural information and removes useless textures. First, structural and textural-aware recursive filtering features (STRFs) are extracted along with an exponential form of windowed inherent variance (eWIV). Then, multi-scale STRFs are integrated by the principal component analysis (PCA) method to obtain more discriminative features (MSTRF). Finally, the fused features are fed into a pixel-wise classifier to obtain the final results. The main difference between the MSTRF method and other feature extraction methods is that the MSTRF method can make full use of the proposed eWIV map, which can help to properly characterize structure and texture in HSIs. Experimental results on several public data sets indicate that our method leads to state-of-the-art classification performance, especially in the presence of very small training set.

Convolutional neural networks (CNNs) have been widely employed for hyperspectral image (HSI) classification due to their powerful ability to extract local spatial features. However, CNN-based methods cannot establish long-range dependencies among sequences of pixels. Transformers offer significant advantages when processing sequential data and can establish global relationships, but they still encounter a number of challenges, such as their limited spatial feature extraction ability, or their high computational cost. In order to address the aforementioned issues, we develop a new fast HSI classification approach combining transformers and SimAM-based CNNs. The latter are utilized to extract better spatial features, where the complex spatial characteristics of HSIs are retrieved using an improved hierarchical 2D dense network structure. A dual attention unit (DAU) mechanism is then utilized to direct the model’s attention to discriminative spatial pixel characteristics and effective feature map channels, while suppressing information that is irrelevant for classification purposes. Regarding the spectral features, after extracting hierarchical local characteristics from various convolutional layers (using the hierarchical dense network structure), a squeezed-enhanced axial transformer is employed to establish global long-range dependencies whilst enhancing the ability of the model to extract local detail features in the HSI. Besides, a new Lion optimizer is utilized to improve the classification performance of our model. Our quantitative and comparative experiments on four benchmark datasets demonstrate the effectiveness of the proposed approach provides better classification results than other state-of-the-art approaches. Moreover, our FTSCN also achieves better classification results than other methods in practical scenarios.

Since traditional convolutional neural network (CNN) is used to extract the spectral-spatial features of hyperspectral image (HSI) will result in lots of spatial information redundancy. Octave convolution is used to replace the traditional CNN to reduce spatial redundancy and expand the receptive field. However, the methods based on 3D octave convolution may cause many parameters and the model to be complicated. To address these issues, we propose an HSI classification approach based on a double-branch multi-scale spectral-spatial convolution network (DBMS) in this paper. Firstly, We utilize 2D octave convolution and 3D DenseNet sub-networks with different kernels sizes to extract complex spatial features and spectral features, respectively. Furthermore, a channel attention module and a spectral attention module are employed in this two sub-network respectively, to highlight the important feature areas and specific spectral bands that consist of significant information for the classification. Compared with several other state-of-the-art methods, our proposed method can achieve competitive performance on Salinas Valley (SV) HSI dataset.