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Remote Sens. 2016, 8(11), 894; doi:10.3390/rs8110894

Spatial-Temporal Sub-Pixel Mapping Based on Swarm Intelligence Theory

1
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
3
School of Computer Science, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz, Giles M. Foody, Ioannis Gitas and Prasad S. Thenkabail
Received: 8 April 2016 / Revised: 23 September 2016 / Accepted: 25 October 2016 / Published: 29 October 2016
View Full-Text   |   Download PDF [7300 KB, uploaded 29 October 2016]   |  

Abstract

In the past decades, sub-pixel mapping algorithms have been extensively developed due to the large number of different applications. However, most of the sub-pixel mapping algorithms are based on single-temporal images, and the results are usually compromised without auxiliary information due to the ill-posed problem of sub-pixel mapping. In this paper, a novel spatial-temporal sub-pixel mapping algorithm based on swarm intelligence theory is proposed for multitemporal remote sensing imagery. Swarm intelligence theory involves clonal selection sub-pixel mapping (CSSM), which evolves the solution by emulating the biological advantage of the human immune system, and differential evolution sub-pixel mapping (DESM), which optimizes the solution by intelligent operations and heuristic searching in the solution pool. In addition, considering the under-determined problem of sub-pixel mapping, the spatial-temporal sub-pixel mapping method is used to obtain the distribution information at a fine spatial resolution from the bitemporal image pair, which exactly regularizes the ill-posed problem. Furthermore, the short-interval temporal information and the fine spatial distribution information within the bitemporal image pair can be integrated for further use, such as timely and detailed land-cover change detection (LCCD). To verify the validation of the swarm intelligence theory based spatial-temporal sub-pixel mapping algorithm, the proposed algorithm was compared with several traditional sub-pixel mapping algorithms, in both synthetic and real image experiments. The experimental results confirm that the proposed algorithm outperforms the traditional approaches, achieving a better sub-pixel mapping result both qualitatively and quantitatively, as well as improving the subsequent LCCD performance. View Full-Text
Keywords: spatial-temporal sub-pixel mapping (SSM); swarm intelligence theory; clonal selection sub-pixel mapping (CSSM); differential evolution sub-pixel mapping (DESM); land-cover change detection (LCCD) spatial-temporal sub-pixel mapping (SSM); swarm intelligence theory; clonal selection sub-pixel mapping (CSSM); differential evolution sub-pixel mapping (DESM); land-cover change detection (LCCD)
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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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He, D.; Zhong, Y.; Feng, R.; Zhang, L. Spatial-Temporal Sub-Pixel Mapping Based on Swarm Intelligence Theory. Remote Sens. 2016, 8, 894.

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