SAM-CD: Change Detection in Remote Sensing Using Segment Anything Model (Papers Track)
Faroq ALTam (Elm Company); Thariq Khalid (Elm Company); Athul Mathew (Elm Company); Andrew Carnell (Elm Company); Riad Souissi (Elm Company)
Abstract
In remote sensing, Change Detection (CD) refers to locating surface changes in the same area over time. Changes can occur due to man-made or natural activities, and CD is important for analyzing climate changes. The recent advancements in satellite imagery and deep learning allow the development of affordable and powerful CD solutions. The breakthroughs in computer vision Foundation Models (FMs) bring new opportunities for better and more flexible remote sensing solutions. However, solving CD using FMs has not been explored before and this work presents the first FM-based deep learning model, SAM-CD. We propose a novel model that adapts the Segment Anything Model (SAM) for solving CD. The experimental results show that the proposed approach achieves the state of the art when evaluated on two challenging benchmark public datasets LEVIR-CD and DSIFN-CD.