A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing ImagesReport as inadecuate


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


A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images - Download this document for free, or read online. Document in PDF available to download.

State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China





*

Author to whom correspondence should be addressed.



Academic Editor: Felipe Gonzalez Toro

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





Author: Bin Hou, Yunhong Wang and Qingjie Liu *

Source: http://mdpi.com/



DOWNLOAD PDF




Related documents