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Remote Sens. 2017, 9(7), 709;

Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery

1, 2 and 2,*
Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong, China
School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an 710000, China
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez and Prasad S. Thenkabail
Received: 11 May 2017 / Revised: 16 June 2017 / Accepted: 5 July 2017 / Published: 10 July 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
PDF [2182 KB, uploaded 12 July 2017]


Moderate Resolution Imaging Spectroradiometer (MODIS) data are effective and efficient for monitoring urban dynamics such as urban cover change and thermal anomalies, but the spatial resolution provided by MODIS data is 500 m (for most of its shorter spectral bands), which results in difficulty in detecting subtle spatial variations within a coarse pixel—especially for a fast-growing city. Given that the historical land use/cover products and satellite data at finer resolution are valuable to reflect the urban dynamics with more spatial details, finer spatial resolution images, as well as land cover products at previous times, are exploited in this study to improve the change detection capability of coarse resolution satellite data. The proposed approach involves two main steps. First, pairs of coarse and finer resolution satellite data at previous times are learned and then applied to generate synthetic satellite data with finer spatial resolution from coarse resolution satellite data. Second, a land cover map was produced at a finer spatial resolution and adjusted with the obtained synthetic satellite data and prior land cover maps. The approach was tested for generating finer resolution synthetic Landsat images using MODIS data from the Guangzhou study area. The finer resolution Landsat-like data were then applied to detect land cover changes with more spatial details. Test results show that the change detection accuracy using the proposed approach with the synthetic Landsat data is much better than the results using the original MODIS data or conventional spatial and temporal fusion-based approaches. The proposed approach is beneficial for detecting subtle urban land cover changes with more spatial details when multitemporal coarse satellite data are available. View Full-Text
Keywords: land cover change; downscaling; sub-pixel change detection; machine learning; MODIS; Landsat land cover change; downscaling; sub-pixel change detection; machine learning; MODIS; Landsat

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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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Xu, Y.; Lin, L.; Meng, D. Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery. Remote Sens. 2017, 9, 709.

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