Semantic-CD: Remote Sensing Image Semantic Change Detection towards Open-vocabulary Setting

• URL: http://arxiv.org/abs/2501.06808v1
• Date: Sun, 12 Jan 2025 13:22:11 GMT
• Title: Semantic-CD: Remote Sensing Image Semantic Change Detection towards Open-vocabulary Setting
• Authors: Yongshuo Zhu, Lu Li, Keyan Chen, Chenyang Liu, Fugen Zhou, Zhenwei Shi,
• Abstract summary: Traditional change detection methods often face challenges in generalizing across semantic categories in practical scenarios. We introduce a novel approach called Semantic-CD, specifically designed for semantic change detection in remote sensing images. By utilizing CLIP's extensive vocabulary knowledge, our model enhances its ability to generalize across categories.
• Score: 19.663899648983417
• License:
• Abstract: Remote sensing image semantic change detection is a method used to analyze remote sensing images, aiming to identify areas of change as well as categorize these changes within images of the same location taken at different times. Traditional change detection methods often face challenges in generalizing across semantic categories in practical scenarios. To address this issue, we introduce a novel approach called Semantic-CD, specifically designed for semantic change detection in remote sensing images. This method incorporates the open vocabulary semantics from the vision-language foundation model, CLIP. By utilizing CLIP's extensive vocabulary knowledge, our model enhances its ability to generalize across categories and improves segmentation through fully decoupled multi-task learning, which includes both binary change detection and semantic change detection tasks. Semantic-CD consists of four main components: a bi-temporal CLIP visual encoder for extracting features from bi-temporal images, an open semantic prompter for creating semantic cost volume maps with open vocabulary, a binary change detection decoder for generating binary change detection masks, and a semantic change detection decoder for producing semantic labels. Experimental results on the SECOND dataset demonstrate that Semantic-CD achieves more accurate masks and reduces semantic classification errors, illustrating its effectiveness in applying semantic priors from vision-language foundation models to SCD tasks.

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