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.

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.

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.

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.

Feature extraction provides an effective tool to classify hyperspectral images (HSIs). However, most hyperspectral feature extraction methods tend to yield an over-smoothed phenomenon, which leads to inconsistency between the homogeneous regions and the ground objects in the actual scene. To alleviate this problem, an edge-aware feature extractor called contour structural profiles (CSPs) is proposed to extract the discriminative features for hyperspectral images classification (HSIC). The proposed classification method comprises three components. First, the spectral dimension of the HSI is reduced with an averaging-based method. Then, an edge-aware total variation (TV) model is constructed to extract the contour structural profile, in which a learned contour probability map is served as one of the major cues in the feature extraction process. Next, multiscale structural profiles (MSSPs) are constructed using the edge-aware TV model with different parameters so as to fully characterize ground objects with different scales. Finally, the MSSPs are fused with a kernel principal component analysis (KPCA) followed by a spectral classifier to obtain the final classification map. Experimental results on several publicly available hyperspectral datasets illustrate that the proposed method obtains superior classification performance over several state-of-the-art classification approaches, especially when the number of training samples is insufficient.

In this paper, a novel edge guided structure extraction method is proposed for hyperspectral images classification, which consists of the following steps: First, the spectral dimension of the hyperspectral image is reduced with an averaging-based method. Then, the structural features is extracted by an extended relative total variation (ERTV) inspired by a learned edge probability map which serves as one of the major cues in the structure extraction process. Finally, the extracted structural features are fed into SVM for classification. Experimental results on two publicly available hyperspectral data sets demonstrate the competitive performance over several state-of-the-art classification approaches.

Person re-identification is an important problem in computer vision, which involves matching appearance of individuals between non-overlapping camera views. In this paper we present a novel appearance-based method for person re-identification problem. Color feature, Gabor, local binary pattern (LBP) are utilized to form a covariance descriptor to handle the difficulties such as varying illumination, viewpoint angle and non-rigid body, then distances of these features are computed to match these individuals. Experimental results over the challenging dataset VIPeR demonstrate that our method obtains competitive performance.