A MODIFIED ADAPTABLE NEAREST FEATURE SPACE CLASSIFIER FOR REMOTE SENSING IMAGES

In this paper, a novel technique, known as a modified adaptable nearest feature space (MANFS) classifier, is proposed for supervised classification of remote sensing images. The original nearest feature space (NFS) may cause misclassification if the test samples are close to the different class training samples which are highly overlapped. Thus it is difficult to discriminate different classes. Compared to the original NFS classifier, we propose a novel MANFS classifier, which can precisely analysis the coverage of the feature space used in NFS and perfectly confine the extensible ranges of each feature space of NFS, to reduce the impact of the overlapping training samples of different categories. Experimental results show that MANFS achieves better classification accuracy than the original NFS one for remote sensing image.