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Sensors 2017, 17(6), 1364; doi:10.3390/s17061364

Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

1
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2
College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310023, China
3
Huawei Technologies Co., Ltd., Nanjing 210012, China
*
Author to whom correspondence should be addressed.
Received: 4 April 2017 / Revised: 27 May 2017 / Accepted: 8 June 2017 / Published: 12 June 2017
(This article belongs to the Section Remote Sensors)
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Abstract

Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive. View Full-Text
Keywords: superpixel; remote sensing; local compete mechanism; boundary optimization; improved fast Gauss transform; fast marching method superpixel; remote sensing; local compete mechanism; boundary optimization; improved fast Gauss transform; fast marching method
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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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Liu, J.; Tang, Z.; Cui, Y.; Wu, G. Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing. Sensors 2017, 17, 1364.

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