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Lightweight Change Detection in Heterogeneous Remote Sensing Images with Online All-Integer Pruning Training
May 6, 2024, 4:45 a.m. | Chengyang Zhang, Weiming Li, Gang Li, Huina Song, Zhaohui Song, Xueqian Wang, Antonio Plaza
cs.CV updates on arXiv.org arxiv.org
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
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