Multifeature fusion for automatic building change detection in wide-area imagery

Daniel Prince; Paheding Sidike; Almabrok Essa; Vijayan Asari

doi:10.1117/1.JRS.11.026040

28 June 2017 Multifeature fusion for automatic building change detection in wide-area imagery

Daniel Prince, Paheding Sidike, Almabrok Essa, Vijayan Asari

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 2, 026040 (June 2017). https://doi.org/10.1117/1.JRS.11.026040

Abstract

A strategy for detecting changes in known building regions in multitemporal visible and near-infrared imagery based on a linear combination of independent features is presented. Features identified for building and background detection include vegetation, texture, shadow intensity, and distance from known road areas. The resulting building candidates are classified by shape using a unique difference of Gaussian technique. Building regions reported in the reference dataset that indicate the initial observation time are revisited to check for changes in building candidates not identified in the feature fusion strategy. The performance of the proposed technique is tested on real-world aerial imagery and is evaluated visually and quantitatively. Compared with the gradient and normalized difference vegetation index-based building detection methods, the proposed fusion methodology yields better results. For building detection, it provided a completeness result of an average 82.08% and building change detection completeness result of an average 85.67% in our evaluations with five sample images, which included rural, suburban, and urban areas.

Citation Download Citation

Daniel Prince, Paheding Sidike, Almabrok Essa, and Vijayan Asari "Multifeature fusion for automatic building change detection in wide-area imagery," Journal of Applied Remote Sensing 11(2), 026040 (28 June 2017). https://doi.org/10.1117/1.JRS.11.026040

Received: 6 February 2017; Accepted: 8 June 2017; Published: 28 June 2017

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