Huo Hongyuan, Guo Jifa, Li Zhao-Liang
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.
Sensors (Basel). 2018 Jan 26;18(2):363. doi: 10.3390/s18020363.
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.
很少有研究使用II型模糊集来检验高光谱遥感图像分类。本文基于一种高光谱遥感技术，采用改进的区间II型模糊c均值（IT2FCM*）方法进行图像分类。在本研究中，与其他基于传统模糊c均值的方法不同，IT2FCM算法考虑了区间数的排序和光谱不确定性。基于高光谱数据集，使用FCM、IT2FCM和所提出的改进IT2FCM算法进行分类的结果表明，根据聚类精度，IT2FCM方法表现最佳。在本文中，为了验证和证明IT2FCM的可分离性，采用了四个I型模糊有效性指标，并对这些模糊有效性指标在FCM和IT2FCM方法中的应用进行了对比分析。这四个指标也应用于不同空间和光谱分辨率的数据集，以分析光谱和空间缩放因子对FCM、IT2FCM和IT2FCM方法可分离性的影响。来自高光谱数据集的这些有效性指标结果表明，改进的IT2FCM算法在这三种算法中总体上具有最佳值。结果表明，IT2FCM*由于能够处理高光谱不确定性，在高光谱遥感图像分类中表现出良好的性能。
