Combining SAM With Limited Data for Change Detection in Remote Sensing

Abstract

Change detection is a critical task in the remote sensing image (RSI) analysis, widely used in fields such as land cover change and urban planning. With the introduction of foundational models like SAM in computer vision (CV) tasks, their advantages in zero-shot and interactive segmentation have enabled rapid application across diverse visual scenarios. Current research in change detection focuses on designing learnable plug-in modules and fine-tuning foundational models using large annotated data. However, constructing comprehensive datasets and designing effective additional modules pose significant challenges, leading to high costs. To address these issues, we propose a model named Meta-CD for remote sensing change detection (RSCD) with limited data. By introducing a simple fine-tuning module, this model is trained on limited datasets and quickly adapts to change detection tasks. Specifically, we integrate an additional CNN as an adapter with the foundational model FastSAM. Initially, we freeze the parameters of FastSAM and train only the parameters of the introduced adapter and decoder to generate change confidence maps. Subsequently, to enhance the quality of change detection, we introduce a novel pixel-level binarization module that learns the threshold for each pixel in the original image. This module combines the thresholds with the confidence maps to output binary change detection maps, filtering out invalid change pixels. Experimental results demonstrate that our method outperforms other competing approaches on limited datasets and has great zero-shot learning ability. Our code is available at Meta-CD.