Kernel eigenmaps based multiscale sparse model for hyperspectral image classification

Mookambiga, A.; Gomathi, V.

doi:10.1016/j.image.2021.116416

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…This provides significant potential in Earth observation missions, such as land cover mapping [1], precision agriculture [2], land cover classification [3], environmental monitoring [4], and mineral exploration [5]. Several hyperspectral image data processing techniques have been explored, such as denoising [6,7], unmixing [8,9], superresolution [10][11][12][13], target detection [14,15], change detection [16], and classification [17][18][19][20][21]. Among these techniques, HSI classification has attracted more attention.…”

Section: Introductionmentioning

confidence: 99%

S2Former: Parallel Spectral–Spatial Transformer for Hyperspectral Image Classification

Yuan,

Yu,

Qian

et al. 2023

Electronics

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Due to their excellent representation talent in local features, the convolutional neural network (CNN) has achieved favourable performance in hyperspectral image (HSI) classification tasks. Nevertheless, current CNN models exhibit a marked flaw: they are hard to model the dependencies in long-range distanced positions. This flaw becomes more problematic for the HSI classification task, which targets extracting more discriminative features in local and global dimensions from limited samples. In this paper, we introduce a spatial–spectral transformer (S2Former), which explores spatial and spectral feature extraction in a dual-stream framework for HSI Classification. S2Former, which consists of a spatial transformer and a spectral transformer in parallel branches, extracts the discriminative feature in spatial and spectral dimensions. More specifically, we propose multi-head spatial self-attention to capture the long-range spatial dependency of non-adjacent HSI pixels in a spatial transformer. In the spectral transformer, we propose multi-head covariance spectral attention to mine and represent spectral signatures by computing covariance-based channel maps. Meanwhile, the local activation feed-forward network is developed to complement local details. Extensive experiments conducted on four publicly available datasets indicate that our S2Former achieves state-of-the-art performance for the HSI classification task.

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Section: Introductionmentioning

confidence: 99%

S2Former: Parallel Spectral–Spatial Transformer for Hyperspectral Image Classification

Yuan,

Yu,

Qian

et al. 2023

Electronics

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Fuzzy-twin proximal SVM kernel-based deep learning neural network model for hyperspectral image classification

Krishna

Jeya

Deepa

2022

Neural Comput & Applic

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Hierarchical homogeneity-based superpixel segmentation: application to hyperspectral image analysis

Ayres,

de Almeida,

Bermudez

et al. 2024

International Journal of Remote Sensing

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Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data. However, most existing superpixel approaches do not account for specific HI characteristics resulting from its high spectral dimension. In this work, we propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which the technique is based, has been extended hierarchically. Using a novel robust homogeneity testing, the proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity when compared to the classical SLIC segmentation. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks carried out using, respectively, the Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph Convolutional Network (CEGCN) methods. Simulation results with both synthetic and real data show that the technique is competitive with state-of-the-art solutions.

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Kernel eigenmaps based multiscale sparse model for hyperspectral image classification

Cited by 3 publications

References 32 publications

S2Former: Parallel Spectral–Spatial Transformer for Hyperspectral Image Classification

S2Former: Parallel Spectral–Spatial Transformer for Hyperspectral Image Classification

Fuzzy-twin proximal SVM kernel-based deep learning neural network model for hyperspectral image classification

Hierarchical homogeneity-based superpixel segmentation: application to hyperspectral image analysis

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