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DARecNet-BS: Unsupervised Dual-Attention Reconstruction Network for Hyperspectral Band Selection

IEEE Geoscience and Remote Sensing Letters, 2021
Due to the existence of noise and spectral redundancies in hyperspectral images (HSIs), the band selection (BS) is highly required and can be achieved through the attention mechanism. However, existing BS methods fail to consider global interaction between the spectral information and spatial information in a nonlinear fashion.
Swalpa Kumar Roy   +3 more
openaire   +1 more source

BS-SiamRPN: Hyperspectral Video Tracking based on Band Selection and the Siamese Region Proposal Network

2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2022
Shiqing Wang, Kun Qian 0015, Peng Chen
openaire   +1 more source

Hyperspectral band selection via region-aware latent features fusion based clustering

Information Fusion, 2022
Jun Wang, Chang Tang, Zhenglai Li
exaly  

MR-Selection: A Meta-Reinforcement Learning Approach for Zero-Shot Hyperspectral Band Selection

IEEE Transactions on Geoscience and Remote Sensing, 2023
Jie Feng   +2 more
exaly  

BS-ConvKAN: Hyperspectral Band Selection Based on Sparsified KAN Convolution and Hybrid Teacher Guidance

IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium
Kangfei Shen   +4 more
openaire   +1 more source

Hyperspectral Band Selection via Optimal Neighborhood Reconstruction

IEEE Transactions on Geoscience and Remote Sensing, 2020
Qi ï¼·ang, Fahong Zhang, Xuelong Li
exaly  

Multi-Objective Unsupervised Band Selection Method for Hyperspectral Images Classification

IEEE Transactions on Image Processing, 2023
Xianfeng Ou, Meng Wu, Bing Tu
exaly  

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