A Multicenter Soft Supervised Classification Method for Modeling Spectral Diversity in Multispectral Remote Sensing Data

Due to the spectral diversity of objects within the same classes and the spectral similarity between different classes, classical fuzzy classification methods perform poorly in multispectral remote sensing image classification. The basic reason is that they often use a single spectral curve to represent a land cover type while ignoring spectral diversity characteristics. To solve this issue, this article proposes a novel multicenter supervised fuzzy classification (MCSFC) method for modeling spectral diversity in multispectral remote sensing data. Images are first split into clusters with similar sizes or volumes, namely, granularities, by a hierarchical clustering process. Second, the granularities are labeled by training samples. As a result, the centers of the granularities corresponding to one type of sample can represent the spectral diversity within one class and, thus, are treated as the multiple centers of a land cover type. The membership degree of each unlabeled sample belonging to any land cover type is determined by the shortest distance to the centers of each type, and the spectral diversity is considered in this stage. A comparison reveals that the proposed method clearly improves the classification performance of various single-center fuzzy semisupervised clustering (FSSC) methods. Two case studies indicate that the granularity volume parameter notably affects the classification performance, and the overall accuracy (OA) decreases with increasing granularity volume.

Automated summary

This article proposes a novel multicenter supervised fuzzy classification method to model spectral diversity in multispectral remote sensing data. By clustering and labeling, it effectively improves classification accuracy and provides better representation for multiple centers of land cover types.