EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

6 Aug 2017 Savas Ozkan Berk Kaya Gozde Bozdagi Akar

Data acquired from multi-channel sensors is a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low spatial resolution is a critical limitation for previous sensors and the constituent materials of a scene can be mixed in different fractions due to their spatial interactions... (read more)

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