A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion

• Published in: Remote sensing of environment Vol. 199; pp. 241 - 255
• Main Authors: Wu, Chen, Du, Bo, Cui, Xiaohui, Zhang, Liangpei
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 15.09.2017; Elsevier BV
• Subjects: Alarms; Bayesian analysis; Bayesian soft fusion; Change detection; Classification; False alarms; Image classification; Image detection; Iterative slow feature analysis; Land cover; Land use; Land-use/land-cover; Landsat; Landsat satellites; Mapping; Multi-temporal; Pixels; Post-classification; Probability; Remote sensing; Satellite imagery; Studies; Vector analysis; Post-classification; Land-use/land-cover; Change detection; Bayesian soft fusion; Landsat; Iterative slow feature analysis; Multi-temporal
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2017.07.009

Post-classification with multi-temporal remote sensing images is one of the most popular change detection methods, providing the detailed “from-to” change information in real applications. However, due to the fact that it neglects the temporal correlation between corresponding pixels in multi-temporal images, the post-classification approach usually suffers from an accumulation of misclassification errors. In order to solve this problem, previous studies have separated the change and non-change candidates with change vector analysis, and they have only updated the classes of the changed pixels with the post-classification; however, this approach with thresholding loses the continuous change intensity information, where larger values indicate higher probability to be changed. Therefore, in this paper, a new post-classification method with iterative slow feature analysis (ISFA) and Bayesian soft fusion is proposed to obtain reliable and accurate change detection maps. The proposed method consists of three main steps: 1) independent classification is implemented to obtain the class probability for each image; 2) the ISFA algorithm is used to obtain the continuous change probability map of multi-temporal images, where the value of each pixel indicates the probability to be changed; and 3) based on Bayesian theory, the a posteriori probabilities for the class combinations of coupled pixels are calculated to integrate the class probability with the change probability, which is named as Bayesian soft fusion. The class combination with the maximum a posteriori probability is then determined as the change detection result. In addition, a class probability filter is proposed to avoid the false alarms caused by the spectral variation within the same class. Two experiments with multi-temporal Landsat Thematic Mapper (TM) images indicated that the proposed method achieves a clearly higher change detection accuracy than the current state-of-the-art methods. The proposed method based on Bayesian theory and ISFA was also verified to have the ability to improve the change detection rate and reduce the false alarms at the same time. Given its effectiveness and flexibility, the proposed method could be widely applied in land-use/land-cover change detection and monitoring at a large scale. •A new post-classification method was proposed for accurate change map.•Iterative slow feature analysis was utilized to get the change probability.•Class probability from classification was integrated with change probability.•Bayesian soft fusion was proposed to fuse the probability information.•The accuracies of change detection and transition identification are improved.