Local and global spectral and spatial information is crucial for hyperspectral image (HSI) classification. However, modeling the global context has been challenging due to the limitations of receptive fields and quadratic complexity. Mamba’s ability to leverage long-range dependencies with linear computational complexity offers an effective approach to alleviate this issue; however, it does lead to the loss of local detail information. To address this challenge, we propose a novel local-to-global Mamba for HSI classification, termed MambaLG. MambaLG consists of a dual-branch strategy, comprising two core modules: a local and global spatial modeling module (SpaM) and a short- and long-range spectral dynamic perception module (SpeM). In the SpaM, the local and global spatial information is sequentially extracted and integrated, aiming to capture global spatial semantics while preserving the integrity of local 2-D spatial structures. In the SpeM, we utilize local spectral extraction, spectral grouping, and spectral dynamic correlation clustering (SDCC) modules, leveraging Mamba’s strengths in exploring long-range dependencies for more precise short- and long-range spectral feature modeling. Additionally, we introduce a gate attention unit into MambaLG and design a more efficient and interpretable manner for merging spatial and spectral features. Experimental results across multiple datasets (encompassing urban and agricultural scenes) indicate that MambaLG surpasses state-of-the-art algorithms regarding classification accuracy (CA) and inference speed. Comprehensive ablation studies substantiate the advantages of MambaLG in modeling local and global spatial context, enhancing short- and long-range spectral perception, and fusing spatial and spectral information. The codes will be openly available athttps://github.com/danfenghong/IEEE_TGRS_MambaLGto facilitate the reproduction of experimental results.

To relieve perceived pain in patients undergoing colonoscopy, we developed an endoscopist-patient co-participative VR tool (EPC-VR) based on A Neurocognitive Model of Attention to Pain. It allows the patient to play a VR game actively and supports the endoscopist in triggering a distraction mechanism to divert the patient's attention away from the medical procedure. We performed a comparative clinical study with 40 patients. Patients' perception of pain and affective responses were evaluated, and the results support the effectiveness of EPC-VR: active VR playing with endoscopists' participation can help relieve the perceived pain and scare of patients undergoing colonoscopy. Finally, 87.5% of patients opt to use the VR application in the next colonoscopy.

Spatial information is crucial in deep spectral–spatial hyperspectral image (HSI) classification methods. Spatial features can be divided into central features and surrounding features, which have different significance in high-precision HSI classification tasks. However, the existing algorithms ignore the discrepancy between the two attributes, which weaken decisive characteristics and lose spatial structures. To resolve the above issues, a patch-based anti-hierarchical encoder–decoder network is proposed, namely expansion and stripping network (ESNet). First, a surrounding relation module (SRM) is proposed to change the patch scale. Unlike existing scale change modules, the SRM increases the scale in the encoder and decreases the scale in the decoder by controlling the expansion and stripping of peripheral pixels of the patch, which enables ESNet to amply exploit the critical central features while utilizing the surrounding information in the anti-hierarchical structure. Second, we design a spectral–spatial attention module (SSAM) to extract features. To better represent land cover attributes, SSAM employs large- and small-kernel convolutions to generate spatial, spectral, and spectral–spatial weighted features. Finally, a novel skip connection [deep shallow feature fusion module (DSFM)] is designed to promote the model to fuse deep and shallow features while preserving spectral sequences of patches. Combining DSFM with the encoder–decoder structure can more scientifically fuse deep semantics and shallow details. We conduct extensive experiments in six commonly used HSI datasets to demonstrate the superiority of the proposed model. The codes will be available from the website.

Currently, long-range spectral and spatial dependencies have been widely demonstrated to be essential for hyperspectral image (HSI) classification. Due to the transformer superior ability to exploit long-range representations, the transformer-based methods have exhibited enormous potential. However, existing transformer-based approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) treating HSI as 1D sequences neglects spatial properties of HSI, 2) the dependence between spectral and spatial information is not fully considered. To tackle the above problems, a large kernel spectral-spatial attention network (LKSSAN) is proposed to capture the long-range 3D properties of HSI, which is inspired by the visual attention network (VAN). Specifically, a spectral-spatial attention module is first proposed to effectively exploit discriminative 3D spectral-spatial features while keeping the 3D structure of HSI. This module introduces the large kernel attention (LKA) and convolution feed-forward (CFF) to flexibly emphasize, model, and exploit the long-range 3D feature dependencies with lower computational pressure. Finally, the features from the spectral-spatial attention module are fed into the classification module for the optimization of 3D spectral-spatial representation. To verify the effectiveness of the proposed classification method, experiments are executed on four widely used HSI data sets. The experiments demonstrate that LKSSAN is indeed an effective way for long-range 3D feature extraction of HSI.

As an essential reprocessing method, dimensionality reduction (DR) can reduce the data redundancy and improve the performance of hyperspectral image (HSI) classification. A novel unsupervised DR framework with feature interpretability, which integrates both band selection (BS) and spatial noise reduction method, is proposed to extract low-dimensional spectral-spatial features of HSI. We proposed a new Neighboring band Grouping and Normalized Matching Filter (NGNMF) for BS, which can reduce the data dimension whilst preserve the corresponding spectral information. An enhanced 2-D singular spectrum analysis (E2DSSA) method is also proposed to extract the spatial context and structural information from each selected band, aiming to decrease the intra-class variability and reduce the effect of noise in the spatial domain. The support vector machine (SVM) classifier is used to evaluate the effectiveness of the extracted spectral-spatial low-dimensional features. Experimental results on three publicly available HSI datasets have fully demonstrated the efficacy of the proposed NGNMF-E2DSSA method, which has surpassed a number of state-of-the-art DR methods.

Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between data for image classification. However, gravitation is hard to combine with spatial information and rarely been applied in HSI classification. This paper proposes a Bayesian Gravitation based Classification (BGC) to integrate the spectral and spatial information of local neighbors and training samples. In the BGC method, each testing pixel is first assumed as a massive object with unit volume and a particular density, where the density is taken as the data mass in BGC. Specifically, the data mass is formulated as an exponential function of the spectral distribution of its neighbors and the spatial prior distribution of its surrounding training samples based on the Bayesian theorem. Then, a joint data gravitation model is developed as the classification measure, in which the data mass is taken to weigh the contribution of different neighbors in a local region. Four benchmark HSI datasets, i.e. the Indian Pines, Pavia University, Salinas, and Grss_dfc_2014, are tested to verify the BGC method. The experimental results are compared with that of several well-known HSI classification methods, including the support vector machines, sparse representation, and other eight state-of-the-art HSI classification methods. The BGC shows apparent superiority in the classification of high-resolution HSIs and also flexibility for HSIs with limited samples.