Unsupervised Subpixel Mapping of Remotely Sensed Imagery Based on Fuzzy C-Means Clustering Approach

Abstract

Subpixel mapping (SPM) is a technique to obtain a land cover map with finer spatial resolution than the original remotely sensed imagery. An image-based SPM model that directly uses the original image data as input by integrating both the spectral and spatial information has been demonstrated as a promising SPM model. However, all existing image-based SPM models are based on a supervised approach, since the spectral term in these SPM models is composed of a supervised unmixing method. The endmembers and training samples for different land cover classes must be determined before implementing these supervised SPM algorithms. In this letter, a novel unsupervised image-based SPM model based on the fuzzy c-means (FCM) clustering approach (usFCM_SPM) was proposed. By incorporating the unsupervised unmixing criterion of the FCM clustering algorithm and the maximal land cover spatial-dependence principle, the proposed usFCM_SPM can generate a subpixel land cover map without any prior endmember information. Both synthetic multispectral image and real IKONOS image experiments demonstrate that the usFCM_SPM can generate higher accuracy subpixel land cover maps than the traditional unsupervised pixel-scale classification approaches and the unsupervised pixel-swapping model.