TY - GEN
T1 - Coarse-To-Fine Unsupervised Change Detection for Remote Sensing Images Via Object-Based MRF and Inception UNET
AU - Hou, Xuan
AU - Bai, Yunpeng
AU - Shi, Haonan
AU - Li, Ying
N1 - Funding Information:
∗Ying Li is the corresponding author. This work was supported in part by the National Natural Science Foundation of China under Grant 61871460; in part by the Shaanxi Provincial Key Research and Development Program under Grant 2020KW-003
Publisher Copyright:
© 2022 IEEE
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - change detection
KW - coarse-to-fine model
KW - object-based markov random filed
KW - unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=85131254630&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9746189
DO - 10.1109/ICASSP43922.2022.9746189
M3 - Conference Proceeding (Non-Journal item)
AN - SCOPUS:85131254630
SN - 9781665405409
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3288
EP - 3292
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - IEEE Press
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -