Incenter-Based Nearest Feature Space Method for Hyperspectral Image Classification using GPU

In this paper a novel technique based on nearest feature space (NFS), known as incenter-based nearest feature space (INFS), is proposed for supervised hyperspectral image classification. Due to the class separability and neighborhood structure, the traditional NFS can perform well for classification of remote sensing images. However, in some instances, the overlapping training samples might cause classification errors in spite of the high classification accuracy of NFS for normal cases. In response, the INFS is proposed to overcome this problem in this paper. INFS method makes use of the incircle of a triangle which is tangent to its three sides and form a INFS. In addition, an incenter can be calculated by three training samples of the same class efficiently. Furthermore, in order to speed up the computation performance, this paper proposes a parallel computing version of INFS, namely parallel INFS (PINFS). It uses a modern graphics processing unit (GPU) architecture with NVIDIA's compute unified device architecture (CUDA) technology to improve the computational speed of INFS. Experimental results demonstrate the proposed INFS approach is suitable for land cover classification in earth remote sensing. It can achieve the better performance than NFS classifier when the class sample distribution overlaps. Through the computation of GPU by CUDA, we can also gain better speedup.