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Remote Sens. 2017, 9(9), 960; https://doi.org/10.3390/rs9090960

An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

1
School of Geographic and Environmental Sciences, Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, China
2
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Received: 31 July 2017 / Revised: 5 September 2017 / Accepted: 13 September 2017 / Published: 15 September 2017
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

Interval type-2 fuzzy c-means (IT2FCM) clustering methods for remote-sensing data classification are based on interval type-2 fuzzy sets and can effectively handle uncertainty of membership grade. However, most of these methods neglect the spatial information when they are used in image clustering. The spatial information and spectral indices are useful in remote-sensing data classification. Thus, determining how to integrate them into IT2FCM to improve the quality and accuracy of the classification is very important. This paper proposes an enhanced IT2FCM* (EnIT2FCM*) algorithm by combining spatial information and spectral indices for remote-sensing data classification. First, the new comprehensive spatial information is defined as the combination of the local spatial distance and attribute distance or membership-grade distance. Then, a novel distance metric is proposed by combining this new spatial information and the selected spectral indices; these selected spectral indices are treated as another dataset in this distance metric. To test the effectiveness of the EnIT2FCM* algorithm, four typical validity indices along with the confusion matrix and kappa coefficient are used. The experimental results show that the spatial information definition proposed here is effective, and some spectral indices and their combinations improve the performance of the EnIT2FCM*. Thus, the selection of suitable spectral indices is crucial, and the combination of soil adjusted vegetation index (SAVI) and the Automated Water Extraction Index (AWEIsh) is the best choice of spectral indices for this method. View Full-Text
Keywords: interval type-2 fuzzy set; fuzzy clustering; classification; sentinel-2; remote sensing; multi-spectral remote sensing interval type-2 fuzzy set; fuzzy clustering; classification; sentinel-2; remote sensing; multi-spectral 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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Guo, J.; Huo, H. An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering. Remote Sens. 2017, 9, 960.

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