Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection
Journal:
arXiv
Published Date:
Dec 22, 2024
Abstract
When given two similar images, humans identify their differences by comparing
the appearance ({\it e.g., color, texture}) with the help of semantics ({\it
e.g., objects, relations}). However, mainstream change detection models adopt a
supervised training paradigm, where the annotated binary change map is the main
constraint. Thus, these methods primarily emphasize the difference-aware
features between bi-temporal images and neglect the semantic understanding of
the changed landscapes, which undermines the accuracy in the presence of noise
and illumination variations. To this end, this paper explores incorporating
semantic priors to improve the ability to detect changes. Firstly, we propose a
Semantic-Aware Change Detection network, namely SA-CDNet, which transfers the
common knowledge of the visual foundation models ({\it i.e., FastSAM}) to
change detection. Inspired by the human visual paradigm, a novel dual-stream
feature decoder is derived to distinguish changes by combining semantic-aware
features and difference-aware features. Secondly, we design a single-temporal
semantic pre-training strategy to enhance the semantic understanding of
landscapes, which brings further increments. Specifically, we construct
pseudo-change detection data from public single-temporal remote sensing
segmentation datasets for large-scale pre-training, where an extra branch is
also introduced for the proxy semantic segmentation task. Experimental results
on five challenging benchmarks demonstrate the superiority of our method over
the existing state-of-the-art methods. The code is available at
\href{https://github.com/thislzm/SA-CD}{SA-CD}.
