Multisource data fusion for image classification using fisher criterion based nearest feature space approach

In this paper, a novel technique, known as nearest feature space (NFS) approach, is proposed for supervised classification of multisource images for the purpose of landslide hazard assessment. It is developed for land cover classification based on the fusion of remotely sensed images of the same scene collected from multiple sources. This approach presents a framework for data fusion of multisource remotely sensed images, which consists of two approaches, referred to as band generation process (BGP) and Fisher criterion based NFS classifier. Compared to the original NFS, we propose an improve NFS classifier which uses the Fisher criterion of between-class and within-class discrimination to enhance the original one. In the training phase, the labeled samples are discriminated by the Fisher criterion, which can be treated as a pre-processing of NFS. Finally, the classification results can be obtained by NFS algorithm. In order for the proposed NFS to be effective for multispectral images, a multiple adaptation BGP is introduced to create a new set of additional bands especially accommodated to landslide classes. Experimental results demonstrate the proposed BGP/NFS approach is suitable for land cover classification in earth remote sensing and improves the precision of image classification.