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

2017 ◽  
Vol 9 (11) ◽  
pp. 1112 ◽  
Author(s):  
ZhiYong Lv ◽  
WenZhong Shi ◽  
XiaoCheng Zhou ◽  
Jón Benediktsson
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Automatic System ◽  
Remote Sensing Images ◽  
Land Cover Change Detection

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

2018 ◽  
Vol 10 (6) ◽  
pp. 901 ◽  
Author(s):  
Zhiyong Lv ◽  
Tongfei Liu ◽  
Penglin Zhang ◽  
Jón Atli Benediktsson ◽  
Yixiang Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Contextual Information ◽  
Remote Sensing Images ◽  
Land Cover Change Detection

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

2018 ◽  
Author(s):  
Zhiyong Lv ◽  
Tongfei Liu ◽  
Penglin Zhang ◽  
Jón Atli Benediktsson ◽  
Yixiang Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Contextual Information ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Central Pixel ◽  
Inventory Map ◽  
Land Cover Change Detection

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 during 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. While the whole bi-temporal images are scanned pixel-by-pixel, 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 two land cover change cases with Landsat bi-temporal remote sensing images. In comparison to several widely used change detection methods, the proposed approach can achieve a land cover change inventory map with a competitive accuracy.

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