Fusion of multispectral imagery and DSMs for building change detection using belief functions and reliabilities. (2nd January 2019)

• Record Type: Journal Article
• Authors: Tian, Jiaojiao; Dezert, Jean
• Is Part Of: International journal of image and data fusion. Volume 10:Number 1(2019)
• Page Start: 1
• Page End: 27
• Publication Date: 2019-01-02
• Subjects: Change detection -- belief functions -- DSmT -- DST -- DSM; Image processing -- Periodicals; Multisensor data fusion -- Periodicals; Multisensor data fusion; Periodicals; 621.36705
• DOI: 10.1080/19479832.2018.1513957
• Languages: English
• ISSNs: 1947-9832

Abstract

ABSTRACT: The extraction of building changes from very high resolution satellite images is an important but challenging task in remote sensing. Digital Surface Models (DSMs) generated from stereo imagery have proved to be valuable additional data sources for this task. In order to efficiently use the change information from the DSMs and spectral images, belief functions have .been introduced. In this article, two-step building change detection fusion models based on both Dempster-Shafer Theory (DST) and Dezert-Smarandache Theory (DSmT) frameworks are proposed. In the first step, basic belief assignments (BBAs) of the change indicators from images and DSMs are calculated by using a refined sigmoidal BBA model. Then these BBAs are employed for the new proposed building change detection decision fusion approach. In order to cover the miss-detections introduced by the wrong height values of the DSMs and incomplete information from images, disparity maps from the DSM generation procedure and shadow maps from the multispectral channels are adopted to generate reliability maps, which are further integrated to the fusion models. In the last step, building change masks are generated based on four decision-making criteria. In the experimental part of this work, we evaluate the performance of this new building change detection method on real satellite images thanks to a building change reference mask representing the ground truth. Substantial accuracy improvements are achieved when comparing the new results with those obtained from classical 3D change detection approaches.