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Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images


Titill: Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images
Höfundur: Lv, Zhiyong
Liu, Tongfei
Zhang, Penglin
Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Chen, Yixiang
Útgáfa: 2018-06-08
Tungumál: Enska
Umfang: 901
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;10(6)
ISSN: 2072-4292
DOI: 10.3390/rs10060901
Efnisorð: Land cover change detection; Adaptive contextual information; Bi-temporal remote sensing images; Fjarkönnun; Loftmyndir
URI: https://hdl.handle.net/20.500.11815/758

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Tilvitnun:

Lv, Z.; Liu, T.; Zhang, P.; Atli Benediktsson, J.; Chen, Y. Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images. Remote Sens. 2018, 10, 901. doi:10.3390/rs10060901

Útdráttur:

Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy

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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