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Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images

Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images


Titill: Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images
Höfundur: Lv, ZhiYong
Shi, WenZhong
Zhou, XiaoCheng
Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Útgáfa: 2017-10-31
Tungumál: Enska
Umfang: 1112
Háskóli/Stofnun: Háskóli Íslands
University of Iceland
Svið: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Deild: Rafmagns- og tölvuverkfræðideild (HÍ)
Faculty of Electrical and Computer Engineering (UI)
Birtist í: Remote Sensing;9(11)
ISSN: 2072-4292
DOI: 10.3390/rs9111112
Efnisorð: Remote sensing images; Land cover change detection; Semi-automatic change detection system; Fjarkönnun
URI: https://hdl.handle.net/20.500.11815/495

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Tilvitnun:

Lv, Z., Shi, W., Zhou, X., & Benediktsson, J. (2017). Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images. Remote Sensing, 9(11), 1112. doi:10.3390/rs9111112

Útdráttur:

Change detection is an increasingly important research topic in remote sensing application. Previous studies achieved land cover change detection (LCCD) using bi-temporal remote sensing images. However, many widely used methods detected change depending on a series of parameters, and determining parameters is time-consuming. Furthermore, numerous methods are data-dependent. Therefore, their degree of automation should be improved significantly. Three techniques, which consist of a semi-automatic change detection system, are proposed for LCCD to overcome the abovementioned drawbacks. The three techniques are as follows: (1) change magnitude image (CMI) noise reduction is based on Gaussian filter (GF), which is coupled with OTSU for reducing CMI noise automatically using an iterative optimization strategy; (2) a method based on histogram curve fitting is suggested to predict the threshold range for parameter determination; and (3) a modified region growing algorithm is built for iteratively constructing the final change detection map. The detection accuracies of the proposed system are investigated through four experiments with different bi-temporal image scenes. Compared with several widely used change detection methods, the proposed system can be applied to detect land cover change with high accuracy and flexibility. This work is an attempt to provide a change detection system that is compatible with remote sensing images with high and median-low spatial resolution

Leyfi:

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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