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Remote Sens. 2016, 8(2), 124; doi:10.3390/rs8020124

Spectral-Spatial Clustering with a Local Weight Parameter Determination Method for Remote Sensing Imagery

The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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Academic Editors: Ruiliang Pu, Chandra Giri and Prasad S. Thenkabail
Received: 27 September 2015 / Revised: 13 January 2016 / Accepted: 1 February 2016 / Published: 5 February 2016
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Abstract

Remote sensing image clustering is a challenging task considering its intrinsic complexity. Recently, by combining the spectral and spatial information of the remote sensing data, the clustering performance can be dramatically enhanced, termed as Spectral-Spatial Clustering (SSC). However, it has always been difficult to determine the weight parameter for balancing the spectral term and spatial term of the clustering objective function. In this paper, spectral-spatial clustering with a local weight parameter determination method for remote sensing image was proposed, i.e., L-SSC. In L-SSC, considering the large scale of remote sensing images, the weight parameter can be determined locally in a patch image instead of the whole image. Afterwards, the local weight parameter was used in constructing the objective function of L-SSC. Thus, the remote sensing image clustering problem was transformed into an optimization problem. Finally, in order to achieve a better optimization performance, a variant of differential evolution (i.e., jDE) was used as the optimizer due to its powerful optimization capability. Experimental results on three remote sensing images, including a Wuhan TM image, a Fancun Quickbird image, and an Indian Pine AVIRIS image, demonstrated that the proposed L-SSC can acquire higher clustering accuracy in comparison to other spectral-spatial clustering methods. View Full-Text
Keywords: weight parameter; automatic clustering; spectral-spatial clustering; remote sensing weight parameter; automatic clustering; spectral-spatial clustering; remote sensing
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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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Ma, A.; Zhong, Y.; Zhang, L. Spectral-Spatial Clustering with a Local Weight Parameter Determination Method for Remote Sensing Imagery. Remote Sens. 2016, 8, 124.

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