A change detection method using spatial-temporal-spectral information from Landsat images. Issue 2 (17th January 2020)

• Record Type: Journal Article
• Authors: Lin, Yukun; Zhang, Lifu; Wang, Nan; Zhang, Xia; Cen, Yi; Sun, Xuejian
• Is Part Of: International journal of remote sensing. Volume 41:Issue 2(2020)
• Page Start: 772
• Page End: 793
• Publication Date: 2020-01-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1648905
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Remote sensing data and techniques are reliable tools for monitoring land cover and land-use change. For time-series change detection algorithms, detecting the breakpoints accurately is the key element. However, the current state-of-art algorithms are vulnerable to cloud/cloud shadow or noises in the time-series imagery. The objective of this study is to develop a new method to detect land cover change using Landsat imagery by integrating temporal, spectral and spatial information to increase the accuracy of breakpoints detection. In the temporal dimension, the time-series model is decomposed into seasonality and trend. Due to different land cover types corresponding to different seasonal characteristics, breakpoints exist only in the seasonal component. In the spectral dimension, two-step judgement is applied. The first judgement detects a change when the seasonal breakpoint positions are the same in different spectral bands. The second judgement involves detecting a changed pixel when the classification result indicates different types on either side of the breakpoint. In the spatial dimension, neighbour information is utilized to control the false-positive rate. Experimental results using all available Landsat images acquired between 2001 and 2006 in Kansas City, US, illustrate the effectiveness and stability of the proposed approach. All pixels were used for assessing the classification and change detection accuracy compared with National Land Cover Database products. The overall accuracy of classification into eight categories was about 81% and the accuracy of change detection was 88%. Maps of timing of breaks and change times are also provided in this article.