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Open AccessArticle

An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Sanya Institute of Remote Sensing, Hainan 572029, China
4
School of Sciences, Qiqihar University, Qiqihar 161006, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(12), 1863; https://doi.org/10.3390/rs10121863
Received: 14 September 2018 / Revised: 19 November 2018 / Accepted: 20 November 2018 / Published: 22 November 2018
(This article belongs to the Special Issue Advanced Topics in Remote Sensing)
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Abstract

Built-up areas extraction from satellite images is an important aspect of urban planning and land use; however, this remains a challenging task when using optical satellite images. Existing methods may be limited because of the complex background. In this paper, an improved boosting learning saliency method for built-up area extraction from Sentinel-2 images is proposed. First, the optimal band combination for extracting such areas from Sentinel-2 data is determined; then, a coarse saliency map is generated, based on multiple cues and the geodesic weighted Bayesian (GWB) model, that provides training samples for a strong model; a refined saliency map is subsequently obtained using the strong model. Furthermore, cuboid cellular automata (CCA) is used to integrate multiscale saliency maps for improving the refined saliency map. Then, coarse and refined saliency maps are synthesized to create a final saliency map. Finally, the fractional-order Darwinian particle swarm optimization algorithm (FODPSO) is employed to extract the built-up areas from the final saliency result. Cities in five different types of ecosystems in China (desert, coastal, riverside, valley, and plain) are used to evaluate the proposed method. Analyses of results and comparative analyses with other methods suggest that the proposed method is robust, with good accuracy. View Full-Text
Keywords: built-up areas; saliency detection; improved boosting learning; Sentinel-2 built-up areas; saliency detection; improved boosting learning; Sentinel-2
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Sun, Z.; Meng, Q.; Zhai, W. An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images. Remote Sens. 2018, 10, 1863.

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