Sparse Constrained Adaptive Structure Consistency based Unsupervised Image Regression for Heterogeneous Remote Sensing Change Detection
MATLAB Code: SCASC - 2021 This is a test program for the Sparse Constrained Adaptive Structure Consistency based method (SCASC) for heterogeneous change detection.
SCASC is an unsupervised image regression method based on the structure consistency between heterogeneous images. SCASC first adaptively constructs a similarity graph to represent the structure of pre-event image, then uses the graph to translate the pre-event image to the domain of post-event image, and then computes the difference image. Finally, a superpixel-based Markovian segmentation model is designed to segment the difference image into changed and unchanged classes.
Please refer to the paper for details. You are more than welcome to use the code!
===================================================
#2-Texas is download from Professor Michele Volpi's webpage at https://sites.google.com/site/michelevolpiresearch/home.
#3-Img7, #4-Img17, and #7-Img5 can be found at Professor Max Mignotte's webpage (http://www-labs.iro.umontreal.ca/~mignotte/) and they are associated with this paper https://doi.org/10.1109/TGRS.2020.2986239.
#6-California is download from Dr. Luigi Tommaso Luppino's webpage (https://sites.google.com/view/luppino/data) and it was downsampled to 875*500 as shown in our paper.
The graphCut algorithm is download from Professor Anton Osokin's webpage at https://github.com/aosokin/graphCutMex_BoykovKolmogorov.
If you use these resources, please cite their relevant papers.
===================================================
If you use this code for your research, please cite our paper. Thank you!
@ARTICLE{Sun2021Sparse,
author={Sun, Yuli and Lei, Lin and Guan, Dongdong and Li, Ming and Kuang, Gangyao},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Sparse Constrained Adaptive Structure Consistency based Unsupervised Image Regression for Heterogeneous Remote Sensing Change Detection},
year={2021},
volume={},
number={},
pages={},
doi={10.1109/TGRS.2021.3110998}}
In this work, due to the computational complexity, we only consider the forward transformation, i.e., translating the pre-event image to the domain of post-event image. Our future work is to improve its computation efficiency and design an effective fusion strategy to fuse the forward and backward detection results, thus improving the detection performance.
Unzip the Zip files (GC) and run the SCASC demo file (tested in Matlab 2016a)!
If you have any queries, please do not hesitate to contact me (sunyuli@mail.ustc.edu.cn).