Combining the Pixel-based and Object-based Methods for Building Change Detection Using High-resolution Remote Sensing Images

doi:10.11947/j.AGCS.2018.20170483

Abstract

Abstract: Timely and accurate change detection of buildings provides important information for urban planning and management.Accompanying with the rapid development of satellite remote sensing technology,detecting building changes from high-resolution remote sensing images have received wide attention.Given that pixel-based methods of change detection often lead to low accuracy while object-based methods are complicated for uses,this research proposes a method that combines pixel-based and object-based methods for detecting building changes from high-resolution remote sensing images.First,based on the multiple features extracted from the high-resolution images,a random forest classifier is applied to detect changed building at the pixel level.Then,a segmentation method is applied to segement the post-phase remote sensing image and to get post-phase image objects.Finally,both changed building at the pixel level and post-phase image objects are fused to recognize the changed building objects.Multi-temporal QuickBird images are used as experiment data for building change detection with high-resolution remote sensing images,the results indicate that the proposed method could reduce the influence of environmental difference,such as light intensity and view angle,on building change detection,and effectively improve the accuracies of building change detection.

Key words: building change detection, information fusion, random forest, image segmentation, high resolution

CLC Number:

P237

ZHANG Zhiqiang, ZHANG Xinchang, XIN Qinchuan, YANG Xiaoling. Combining the Pixel-based and Object-based Methods for Building Change Detection Using High-resolution Remote Sensing Images[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(1): 102-112.

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