An unsupervised HS-SISR framework trains a super-resolution network on synthetic abundance maps from a dead leaves model derived from the low-resolution input and known PSF, then reconstructs the enhanced hyperspectral image via unmixing.
Uiu-net: U-net in u-net for infrared small object detection
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
FeedbackSTS-Det improves moving infrared small target detection accuracy and reduces false alarms via a closed-loop spatio-temporal semantic feedback strategy and an embedded sparse semantic module that captures long-range dependencies with low overhead.
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Synthetic Abundance Maps for Unsupervised Super-Resolution of Hyperspectral Remote Sensing Images
An unsupervised HS-SISR framework trains a super-resolution network on synthetic abundance maps from a dead leaves model derived from the low-resolution input and known PSF, then reconstructs the enhanced hyperspectral image via unmixing.
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FeedbackSTS-Det: Sparse Frames-Based Spatio-Temporal Semantic Feedback Network for Moving Infrared Small Target Detection
FeedbackSTS-Det improves moving infrared small target detection accuracy and reduces false alarms via a closed-loop spatio-temporal semantic feedback strategy and an embedded sparse semantic module that captures long-range dependencies with low overhead.