Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral Images
A novel unfolding approach for clustering hyperspectral images by transforming an ADMM-based sparse subspace clustering algorithm into a neural network architecture to obtain the self-representation matrix, while incorporating structural priors to preserve the data structure.
Efficient Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering
A multi-level graph subspace contrastive learning framework is proposed to effectively extract local and global features from hyperspectral images and obtain robust graph embeddings for improved clustering performance.
A Unified Knowledge-Embedded Contrastive Learning Framework for Efficient Hyperspectral Image Classification
The proposed knowledge-embedded contrastive learning (KnowCL) framework unifies supervised, unsupervised, and semi-supervised hyperspectral image classification into a scalable end-to-end framework, leveraging labeled and unlabeled data to achieve superior performance compared to existing methods.
Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Images
A novel spatial-spectral reliable contrastive graph convolutional network (S2RC-GCN) is proposed to effectively classify complex land cover scenes using hyperspectral imagery.
Distilled Mixed Spectral-Spatial Network for Accurate Hyperspectral Salient Object Detection
The proposed Distilled Mixed Spectral-Spatial Network (DMSSN) efficiently leverages spectral and spatial information in hyperspectral images to achieve state-of-the-art performance in salient object detection tasks.
Efficient Hyperspectral Image Classification with Bidirectional Feature Extraction and Spatial Processing
The proposed HSIMamba model employs a novel bidirectional feature extraction approach combined with specialized spatial processing to achieve superior classification performance on hyperspectral image data, while maintaining high computational efficiency.
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