Multispectral remote sensing image change detection based on Markovian fusion

• Published in: 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics) pp. 1 - 5
• Main Authors: Qiongcheng Xu, Yunchen Pu, Wei Wang, Huamin Zhong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2012
• Subjects: Accuracy; bayesian decision theory; Bayesian methods; change detection; Change detection algorithms; Gibbs potential function; Markov random fields; Markovian fusion; multispectral remote sensing image; Noise; Remote sensing; Robustness
• DOI: 10.1109/Agro-Geoinformatics.2012.6311725
• ISBN: 1467324957; 9781467324953

This paper presents a novel multispectral remote sensing image change detection (CD) algorithm based on Markovian fusion. This new method intends to obtain the optimal change map (change detection result) by fusing information contained in each band. The optimal change map are modeled as Markov Random Fields (MRF) which takes into account not only the spectral information of multiple bands but also the contextual information of both the pixels in the optimal change map and the relationship between the optimal change map and change maps of each band respectively, and thus, leads to a more accurate and robust change detection result. In the analysis of difference image, an unsupervised threshold selection algorithm based on Bayesian decision theory is introduced, which aims at extracting the changed information from the images. The finding of optimal change map is equivalent to minimizing the total Gibbs potential function by using simulated annealing algorithm. The experimental result of the proposed algorithm compared with the change map of each band is presented, which indicates that the proposed method improves the result effectively and is superior to any band's change map.