Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework

arXiv:2404.12721v1 Announce Type: cross
Abstract: Land-cover mapping is one of the vital applications in Earth observation, aiming at classifying each pixel's land-cover type of remote-sensing images. As natural and human activities change the landscape, the land-cover map needs to be rapidly updated. However, discovering newly appeared land-cover types in existing classification systems is still a non-trivial task hindered by various scales of complex land objects and insufficient labeled data over a wide-span geographic area. In this paper, we propose a …

abstract applications arxiv change cs.ai cs.cv cs.lg earth earth observation few-shot framework generalized however human hybrid images landscape map mapping natural novel observation pixel remote-sensing segmentation semantic sensing type via vital