Object-based change detection from satellite imagery by segmentation optimization and multi-features fusion

• Published in: International journal of remote sensing Vol. 38; no. 13; pp. 3886 - 3905
• Main Authors: Peng, Daifeng, Zhang, Yongjun
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 03.07.2017; Taylor & Francis Ltd
• Subjects: algorithms; Change detection; data collection; Detection; Feature extraction; High resolution; Image detection; Image resolution; Image segmentation; Imagery; Information retrieval; Mathematical models; Object recognition; Optimization; Qualitative analysis; quantitative analysis; Remote sensing; Resolution; Satellite imagery; Satellites; Spaceborne remote sensing
• ISSN: 0143-1161; 1366-5901; 1366-5901
• DOI: 10.1080/01431161.2017.1308033

This article presents a novel object-based change detection (OBCD) approach in high-resolution remote-sensing images by means of combining segmentation optimization and multi-features fusion. In the segmentation optimization, objects with optimized boundaries and proper sizes are generated by object intersection and merging (OIM) processes, which ensures the accurate information extraction from image objects. Within multi-features fusion and change analysis, the Dempster and Shafer (D-S) evidence theory and the Expectation-Maximization (EM) algorithm are implemented, which effectively utilize multidimensional features besides avoiding the selection of an appropriate change threshold. The main advantages of our proposed method lie in the improvement of object boundary and the fuzzy fusion of multi-features information. The proposed approach is evaluated using two different high-resolution remote-sensing data sets, and the qualitative and quantitative analyses of the results demonstrate the effectiveness of the proposed approach.