Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection

• URL: http://arxiv.org/abs/2412.16918v1
• Date: Sun, 22 Dec 2024 08:27:15 GMT
• Title: Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection
• Authors: Yuhang Gan, Wenjie Xuan, Zhiming Luo, Lei Fang, Zengmao Wang, Juhua Liu, Bo Du,
• Abstract summary: We propose a Semantic-Aware Change Detection network, namely SA-CDNet. Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes. We also design a single-temporal semantic pre-training strategy to enhance the semantic understanding of landscapes.
• Score: 41.80924135539708
• License:
• 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}.

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