Hyperspectral Image Classification using Random Kitchen Sink and Regularized Least Squares

Kernel machines has gained considerable attention in the field of remote sensing for solving machine learning tasks, particularly in classification. Despite the fact that, kernel based methods produce comparatively better performance than traditional learning approaches, they are computationally expensive and requires large memory storage. In recent years, the concept of random features was introduced in kernel machines to solve this problem. This paper presents a new framework for hyperspectral image classification using Random Kitchen Sink (RKS) and Regularized Least Squares (RLS) classifier. The study shows that randomized features are economically powerful tool for hyperspectral image classification which produces significant improvement in classification accuracy. The proposed approach is tested on two standard hyperspectral datasets namely, Salinas-A and Indian Pines subset scene acquired by Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor. A statistical comparison of the accuracies obtained on standard hyperspectral data with and without using Random Kitchen Sink algorithm for Regularized Least Squares classifier is analysed to show the effectiveness of the proposed method. The experimental results shows that the proposed method leads to improvement in Overall Accuracy from 85.12% to 98.58% and Kappa Coefficient from 0.8154 to 0.9822 for Salinas-A data scene. The analysis of Indian Pines subset scene shows that the proposed work results in significant improvement in Overall Accuracy from 62.76% to 93.79% and Kappa Coefficient from 0.5061 to 0.9160. The result analysis proves that random features of hyperspectral data as input to a standard linear classifier without the aid of any preprocessing produces better classification accuracy.