Noise Clustering-Based Hypertangent Kernel Classifier for Satellite Imaging Analysis

The classification accuracy and the computational complexity are degraded by the occurrence of nonlinear data and mixed pixels present in satellite images. Therefore, the kernel-based fuzzy classifiers are required for the separation of linear and nonlinear data. This paper presents two classifiers for handling the nonlinear separable data and mixed pixels. The classifiers, noise clustering (NC) and NC with hypertangent kernels (NCH), are used for handling these problems in the satellite images. In this study, a comparative study between NC and NCH has been carried out. The membership values of KFCM are obtained to produce the final result. It is found that the proposed classifiers achieved good accuracy. It is observed that there is an enhancement in the classification accuracy by using NC and NCH. The maximum accuracy achieved for NC and NCH is 75% at delta = 0.7, delta = 0.5, respectively. After comparing both the results, it has identified that NCH gives better results. The classification of Formosat-2 data is done by obtaining optimized values of m and delta to generate the fractional outputs. The classification accuracy is performed by using the error matrix with the incorporation of hard classifier and alpha-cut.