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cs.CV2021
Building Damage Mapping with Self-PositiveUnlabeled Learning
Junshi Xia, Naoto Yokoya, Bruno Adriano
Humanitarian organizations must have fast and reliable data to respond to disasters. Deep learning approaches are difficult to implement in real-world disasters because it might be…
eess.IV2021
Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing
Danfeng Hong, Lianru Gao, Jing Yao +4
Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing, yet their ability to simultaneous…
cs.CV2021
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Danfeng Hong, Wei He, Naoto Yokoya +5
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to proce…