Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images
| Autor: | Tongfei Liu, Jon Atli Benediktsson, Zhiyong Lv, Penglin Zhang, Yixiang Chen |
|---|---|
| Přispěvatelé: | Rafmagns- og tölvuverkfræðideild (HÍ), Faculty of Electrical and Computer Engineering (UI), Verkfræði- og náttúruvísindasvið (HÍ), School of Engineering and Natural Sciences (UI), Háskóli Íslands, University of Iceland |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
bi-temporal remote sensing images
010504 meteorology & atmospheric sciences Computer science Science 0211 other engineering and technologies Binary number Magnitude (mathematics) geoinformatics 02 engineering and technology Land cover 01 natural sciences Loftmyndir 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Adaptive contextual information Pixel Bi-temporal remote sensing images land cover change detection adaptive contextual information Euclidean distance Fjarkönnun Remote sensing (archaeology) General Earth and Planetary Sciences Land cover change detection Pairwise comparison Change detection |
| Zdroj: | Remote Sensing, Vol 10, Iss 6, p 901 (2018) Remote Sensing; Volume 10; Issue 6; Pages: 901 |
| Popis: | 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 This work was supported by the National Science Foundation China (61701396), the Natural Science Foundation of Shaan Xi Province (2017JQ4006), Engineering Research Center of Geospatial Information and Digital Technology, NASG (SIDT20171003), The National Key Research and Development Program of China(018YFF0215006), Natural Science Foundation of Jiangsu Province, China (BK20150835), and Tibet Natural Science Foundation-The study of Tibet crop condition monitoring based on crop growth model and multi-source remote sensing data (2016-ZR-15-18). |
| Databáze: | OpenAIRE |
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