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Remote Sens. 2017, 9(3), 284;

Supervised Sub-Pixel Mapping for Change Detection from Remotely Sensed Images with Different Resolutions

1,* , 2
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39759, USA
Author to whom correspondence should be addressed.
Academic Editors: Lizhe Wang, Peng Liu, Liping Di, Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 31 January 2017 / Revised: 9 March 2017 / Accepted: 15 March 2017 / Published: 17 March 2017
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
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Due to the relatively low temporal resolutions of high spatial resolution (HR) remotely sensed images, land-cover change detection (LCCD) may have to use multi-temporal images with different resolutions. The low spatial resolution (LR) images often have high temporal repetition rates, but they contain a large number of mixed pixels, which may seriously limit their capability in change detection. Soft classification (SC) can produce the proportional fractions of land-covers, on which sub-pixel mapping (SPM) can construct fine resolution land-cover maps to reduce the low-spatial-resolution-problem to some extent. Thus, in this paper, sub-pixel land-cover change detection with the use of different resolution images (SLCCD_DR) is addressed based on SC and SPM. Previously, endmember combinations within pixels are ignored in the LR image, which may result in flawed fractional differences. Meanwhile, the information of a known HR land-cover map is insignificantly treated in the SPM models, which leads to a reluctant SLCCD_DR result. In order to overcome these issues, a novel approach based on a back propagation neural network (BPNN) with different resolution images (BPNN_DR) is proposed in this paper. Firstly, endmember variability per pixel is considered during the SC process to ensure the high accuracy of the derived proportional fractional difference image. After that, the BPNN-based SPM model is constructed by a complete supervised framework. It takes full advantage of the prior known HR image, whether it predates or postdates the LR image, to train the BPNN, so that a sub-pixel change detection map is generated effectively. The proposed BPNN_DR is compared with four state-of-the-art methods at different scale factors. The experimental results using both synthetic data and real images demonstrated that it can outperform with a more detailed change detection map being produced. View Full-Text
Keywords: soft classification; sub-pixel mapping (SPM); back-propagation neural network (BPNN); change detection soft classification; sub-pixel mapping (SPM); back-propagation neural network (BPNN); change detection

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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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Wu, K.; Du, Q.; Wang, Y.; Yang, Y. Supervised Sub-Pixel Mapping for Change Detection from Remotely Sensed Images with Different Resolutions. Remote Sens. 2017, 9, 284.

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