Remote sensing image semantic change detection method based on text assistance and comparative learning

Abstract

The invention relates to a semantic change detection method based on text assistance and comparative learning. The method comprises the following steps: 1, acquiring remote sensing images acquired in different time phases in the same area, and inputting a twin multi-scale encoder with shared parameters to extract multi-stage features; 2, a pre-training vision-language model and a text encoder are introduced in the training stage, a text describing changes is generated, high-level visual features are injected, migration features are obtained, and reconstruction constraints based on comparative learning are constructed; 3, constructing a context and channel perception fusion module to carry out adaptive fusion on the multi-scale features; 4, designing a multi-scale decoder to recover the spatial resolution and outputting a semantic change graph; and 5, performing end-to-end optimization on the network by adopting a joint training target consisting of spatial mask supervision loss and reconstruction loss. The method is suitable for scenes such as remote sensing monitoring, disaster assessment and urban dynamic analysis.

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