Building change detection method combining object feature guidance and multiple attention mechanism
High-resolution remote sensing images have rich detail features,and building changes are of variable types with large scale differences.Aiming at the problem that building changes are prone to voids and omissions in complex environments,a building change detection method combining object feature guidance and multiple attention mechanism is proposed to realize fine change information extraction from high-resolution images by enhancing category information through building target-level guidance.The method consists of a building significant enhancement module and a target-guided multi-attention module,which extracts the key areas of the building through global deep feature perception and fusion,combines the target-level feature guid-ance and multiple self-attention to strengthen the feature expression,enhances the contextual feature correlation,effectively re-duces the loss of detailed features,and solves the problem of loss of details caused by the target voids and unclear edges.It is shown through two sets of experiments that this method can improve the accuracy,effectively reduce the change loss in scenes with more kinds of changes,and improve the stability of the algorithm.
NETL
NSTL
万方数据
