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Sensors 2018, 18(2), 363;

Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach

Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
College of Geography and Environment, Tianjin Normal University, Tianjin 300387, China
ICube, CNRS, Université de Strasbourg, 300 Boulevard Sébastien Brant, CS10413, 67412 Illkirch, France
Authors to whom correspondence should be addressed.
Received: 9 November 2017 / Revised: 15 January 2018 / Accepted: 24 January 2018 / Published: 26 January 2018
(This article belongs to the Special Issue Analysis of Multispectral and Hyperspectral Data)
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Few studies have examined hyperspectral remote-sensing image classification with type-II fuzzy sets. This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. In this study, in contrast to other traditional fuzzy c-means-based approaches, the IT2FCM* algorithm considers the ranking of interval numbers and the spectral uncertainty. The classification results based on a hyperspectral dataset using the FCM, IT2FCM, and the proposed improved IT2FCM* algorithms show that the IT2FCM* method plays the best performance according to the clustering accuracy. In this paper, in order to validate and demonstrate the separability of the IT2FCM*, four type-I fuzzy validity indexes are employed, and a comparative analysis of these fuzzy validity indexes also applied in FCM and IT2FCM methods are made. These four indexes are also applied into different spatial and spectral resolution datasets to analyze the effects of spectral and spatial scaling factors on the separability of FCM, IT2FCM, and IT2FCM* methods. The results of these validity indexes from the hyperspectral datasets show that the improved IT2FCM* algorithm have the best values among these three algorithms in general. The results demonstrate that the IT2FCM* exhibits good performance in hyperspectral remote-sensing image classification because of its ability to handle hyperspectral uncertainty. View Full-Text
Keywords: hyperspectral remote sensing; land cover; interval type-II fuzzy set; classification hyperspectral remote sensing; land cover; interval type-II fuzzy set; classification

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Huo, H.; Guo, J.; Li, Z.-L. Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach. Sensors 2018, 18, 363.

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