Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations. Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data, which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects. After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals, an interval-valued fuzzy c-means (FCM) clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study. Such a process can significantly improve the clustering effect; specifically, the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results. Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region, China, and the TM sensor for Yushu, Qinghai, China, were conducted, as well as experiments involving the conventional FCM algorithm, the results of which were used for comparative analysis. Next, a supervised classification method was used to validate the clustering results. The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.