Structure-Preserved and Weakly Redundant Band Selection for Hyperspectral Imagery

Fu, Baijia; Sun, Xudong; Cui, Chuanyu; Zhang, Jiahua; Shang, Xiaodi

doi:10.1109/jstars.2024.3425906

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

2024

DOI: 10.1109/jstars.2024.3425906

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Structure-Preserved and Weakly Redundant Band Selection for Hyperspectral Imagery

Baijia Fu,

Xudong Sun,

Chuanyu Cui

et al.

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2024

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Cited by 3 publications

References 55 publications

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Sample Latent Feature-Associated Low-Rank Subspace Clustering for Hyperspectral Band Selection

Guo,

Zhao,

Sun

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Sample Latent Feature-Associated Low-Rank Subspace Clustering for Hyperspectral Band Selection

Guo,

Zhao,

Sun

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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SSANet-BS: Spectral–Spatial Cross-Dimensional Attention Network for Hyperspectral Band Selection

Cui,

Sun,

et al. 2024

Remote Sensing

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Band selection (BS) aims to reduce redundancy in hyperspectral imagery (HSI). Existing BS approaches typically model HSI only in a single dimension, either spectral or spatial, without exploring the interactions between different dimensions. To this end, we propose an unsupervised BS method based on a spectral–spatial cross-dimensional attention network, named SSANet-BS. This network is comprised of three stages: a band attention module (BAM) that employs an attention mechanism to adaptively identify and select highly significant bands; two parallel spectral–spatial attention modules (SSAMs), which fuse complex spectral–spatial structural information across dimensions in HSI; a multi-scale reconstruction network that learns spectral–spatial nonlinear dependencies in the SSAM-fusion image at various scales and guides the BAM weights to automatically converge to the target bands via backpropagation. The three-stage structure of SSANet-BS enables the BAM weights to fully represent the saliency of the bands, thereby valuable bands are obtained automatically. Experimental results on four real hyperspectral datasets demonstrate the effectiveness of SSANet-BS.

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DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection

Feng,

Yu,

et al. 2024

Sensors

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Declining cultivated land poses a serious threat to food security. However, existing Change Detection (CD) methods are insufficient for overcoming intra-class differences in cropland, and the accumulation of irrelevant features and loss of key features leads to poor detection results. To effectively identify changes in agricultural land, we propose a Difference-Directed Multi-scale Attention Mechanism Network (DDAM-Net). Specifically, we use a feature extraction module to effectively extract the cropland’s multi-scale features from dual-temporal images, and we introduce a Difference Enhancement Fusion Module (DEFM) and a Cross-scale Aggregation Module (CAM) to pass and fuse the multi-scale and difference features layer by layer. In addition, we introduce the Attention Refinement Module (ARM) to optimize the edge and detail features of changing objects. In the experiments, we evaluated the applicability of DDAM-Net on the HN-CLCD dataset for cropland CD and non-agricultural identification, with F1 and precision of 79.27% and 80.70%, respectively. In addition, generalization experiments using the publicly accessible PX-CLCD and SET-CLCD datasets revealed F1 and precision values of 95.12% and 95.47%, and 72.40% and 77.59%, respectively. The relevant comparative and ablation experiments suggested that DDAM-Net has greater performance and reliability in detecting cropland changes.

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Structure-Preserved and Weakly Redundant Band Selection for Hyperspectral Imagery

Cited by 3 publications

References 55 publications

Sample Latent Feature-Associated Low-Rank Subspace Clustering for Hyperspectral Band Selection

Sample Latent Feature-Associated Low-Rank Subspace Clustering for Hyperspectral Band Selection

SSANet-BS: Spectral–Spatial Cross-Dimensional Attention Network for Hyperspectral Band Selection

DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection

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