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About this paper
Muhammad Ahmad, Gemine Vivone, Ronny Hänsch, Ahmed Sohaib, Manuel Mazzara, Danfeng Hong
LWM classifies hyperspectral pixels by combining spatial and spectral features with a planned sequence of learned latent representations of a static patch. These are not snapshots: a planner weights them, and a classifier combines the planned summary with the representation. In same-scene tests, Dioni overall accuracy rose from 83.88% with SST to 91.71% with full LWM; transfer to new scenes was not tested.
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