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Sensors 2016, 16(9), 1377; doi:10.3390/s16091377

A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China
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Academic Editor: Felipe Gonzalez Toro
Received: 27 June 2016 / Revised: 11 August 2016 / Accepted: 22 August 2016 / Published: 27 August 2016
(This article belongs to the Section Remote Sensors)
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Abstract

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. View Full-Text
Keywords: change detection; remote sensing; extended morphological attribute profiles; saliency; morphological building index change detection; remote sensing; extended morphological attribute profiles; saliency; morphological building index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hou, B.; Wang, Y.; Liu, Q. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images. Sensors 2016, 16, 1377.

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