NDLI: Building Change Detection Based on Markov Random Field: Exploiting Both Pixel and Corner Features

Content Provider	IEEE Xplore Digital Library
Author	Kaibin Zong Sowmya, A. Trinder, J.
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Univ. of New South Sales, Sydney, NSW, Australia (Kaibin Zong; Sowmya, A.) \|\| Sch. of Civil & Environ. Eng., Univ. of New South Sales, Sydney, NSW, Australia (Trinder, J.)
Abstract	Map databases usually suffer from obsolete scene details due to frequently occurring changes, therefore automatic change detection has become vital. Previous research has demonstrated that Markov random field (MRF) is an effective method for image classification, in which both per pixel features and contextual relations between neighbouring points are incorporated into one framework, and the problem solved by means of maximum a posteriori (MAP) criterion. However, with the advent of high resolution images, other types of spatial information (e.g. corners and edges) can also be extracted and treated as clues for detecting changes, which is usually ignored in the previous work. In this paper, we propose a framework for building change detection from high resolution images based on Markov random field that exploits all spectral, spatial and contextual features. The initial detection results are obtained based on pixel level classification and MRF. Following that, corners are extracted and building corner candidates are determined via classification. All candidates are then refined based on previous MRF results and connected by a weighted edge map. Hereafter, building changes are initialized by the area included in the connected corners (refined) and the MRF is optimized again to improve previous outputs. Final results are achieved after some suitable post processing steps. Experimental results demonstrate the capability of the proposed method for building change detection and the usefulness of spatial features.
Starting Page	1
Ending Page	7
File Size	745506
Page Count	7
File Format	PDF
e-ISBN	9781467367950
DOI	10.1109/DICTA.2015.7371244
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-23
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Image edge detection Computational modeling Buildings Feature extraction Classification algorithms Markov random fields
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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