A Novel Change Detection Framework in Urban Area Using Multi-Level Matching Feature and Automatic Sample Extraction Strategy

Yuanxiu Zhou, Yan Song, Songxue Cui, Haitian Zhu, Jie Sun, Wenjun Qin
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Urban change detection (CD) using remote sensing images is of great significance for monitoring and analyzing the spatial and temporal distribution of changes within cities, which can be used to guide urban management. In order to reduce false detections and improve the CD automation, a novel CD framework for high resolution images is proposed in this article. First, a novel multilevel matching feature is presented, combining structural invariant features with multiscale dense matching features
more » ... to comprehensively describe the invariant properties of ground objects at different levels. Second, a newly automatic training sample extraction strategy is proposed, in which sufficient and accurate no-change samples can be obtained by Gaussian-weighted Dempster-Shafer evidence theory and L1-norm, meanwhile, typical change samples can be extracted by sequential spectral change vector analysis. Utilizing the automatic extracted samples, the final CD results are obtained using four supervised classifiers, respectively (k-nearest neighbor, support vector machine, rotation forest, and extra-trees). To validate the proposed CD framework, experiments are conducted in four datasets with spectral variability, spectral confusion between the changed objects and unchanged backgrounds, and misregistration. The results demonstrate that the proposed multilevel matching feature and automatic sample extraction strategy can obtain better results with different types of supervised classifiers, and can effectively improve automation of CD, which is applicable to the large spatial extent scene. Index Terms-Automatic sample extraction strategy, change detection (CD), Gaussian-weighted Dempster-Shafer evidence theory, high resolution image, multilevel matching feature, sequential spectral change vector analysis.
doi:10.1109/jstars.2021.3064311 fatcat:z2qaqs7m2bcpzonmxw7m2uz25u