Coarse-To-Fine Unsupervised Change Detection for Remote Sensing Images Via Object-Based MRF and Inception UNET
Xuan Hou, Yunpeng Bai, Haonan Shi, Ying Li
Abstract
With the rapid development of various satellite sensor techniques, remote sensing imagery has been an important source of data in change detection applications. This paper aims to propose an unsupervised change detection method based on Object-based Markov Random Filed (OMRF) and Inception UNet (IUNet). Our method first utilizes a difference image (DI) obtained from two bi-temporal images as the initial feature, and proposes the OMRF algorithm based on homogeneous region to pre-classify the DI thus derive the coarse change map. The IUNet is then constructed to extract the points with high confidence from the coarse change map for training. Eventually, the trained model is fed to classify the original feature, then the final change map is obtained. Experimental results indicate that our method yields great detection results even without supervision.
BibTeX
@inproceedings{icassp2022_coarsetofineunsu,
title = {Coarse-To-Fine Unsupervised Change Detection for Remote Sensing Images Via Object-Based MRF and Inception UNET},
author = {Xuan Hou and Yunpeng Bai and Haonan Shi and Ying Li},
booktitle = {ICASSP 2022},
year = {2022}
}