Lightweight Change Detection in Heterogeneous Remote Sensing Images with Online All-Integer Pruning Training

arXiv:2405.01920v1 Announce Type: new
Abstract: Detection of changes in heterogeneous remote sensing images is vital, especially in response to emergencies like earthquakes and floods. Current homogenous transformation-based change detection (CD) methods often suffer from high computation and memory costs, which are not friendly to edge-computation devices like onboard CD devices at satellites. To address this issue, this paper proposes a new lightweight CD method for heterogeneous remote sensing images that employs the online all-integer pruning (OAIP) training strategy to efficiently …

abstract arxiv change computation costs cs.cv current detection devices earthquakes edge emergencies images integer memory pruning sensing training transformation type vital