DSNet fuses autoencoder-derived abundance maps with CNN features to classify hyperspectral images, achieving the best reported accuracy on Indian Pines, Berlin, and Augsburg.
Bs3lnet: A new blind-spot self-supervised learning network for hyperspectral anomaly detection,
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Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification
DSNet fuses autoencoder-derived abundance maps with CNN features to classify hyperspectral images, achieving the best reported accuracy on Indian Pines, Berlin, and Augsburg.