Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting

Abstract

The present paper describes the development of a hyperspectral image classification scheme using support vector machines (SVM) with spectrally weighted kernels. The kernels are designed during the training phase of the SVM using optimal spectral weights estimated using the Bacterial Foraging Optimization (BFO) algorithm, a popular modern stochastic optimization algorithm. The optimized kernel functions are then in the SVM paradigm for bi-classification of pixels in hyperspectral images. The effectiveness of the proposed approach is demonstrated by implementing it on three widely used benchmark hyperspectral data sets, two of which were taken over agricultural sites at Indian Pines, Indiana, and Salinas Valley, California, by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) at NASA’s Jet Propulsion Laboratory. The third dataset was acquired using the Reflective Optical System Imaging Spectrometer (ROSIS) over an urban scene at Pavia University, Italy to demonstrate the efficacy of the proposed approach in an urban scenario as well as with agricultural data. Classification errors for One-Against-One (OAO) and classification accuracies for One-Against-All (OAA) schemes were computed and compared to other methods developed in recent times. Finally, the use of the BFO-based technique is recommended owing to its superior performance, in comparison to other contemporary stochastic bio-inspired algorithms.