Integrating Spectral and Spatial features for Hyperspectral Image Classification with a Modified Composite Kernel Framework

Abstract

This paper presents two modified composite kernel frameworks for the hyperspectral image classification. The first proposed framework is a simple extension of the previously proposed generalized composite kernel (GCK) approach, and in the second framework, we have used a voting strategy. The developed modified composite kernels combine different spatial and spectral profiles without considering any weight parameter. We have used the multinomial logistic regression (MLR) algorithm for the classification of hyperspectral images in this work. We have developed different spatial profiles: extended morphological profile (EMP) and extended multiattribute profile (EMAP) in this work, and further integrated them with the spectral profile for spectral-spatial classification. Experimental results with the AVIRIS Indian Pines, ROSIS Pavia university area and AVIRIS Salinas images indicate that the proposed framework leads to an improvement in classification accuracy. The obtained overall accuracy increases by 2–3% for AVIRIS Indian Pines scene, 1–2% for ROSIS Pavia University Area scene and 1–3% for AVIRIS Salinas scene with the first proposed framework. In case of the second proposed framework with a sufficient number of training samples per class, the overall accuracy increases by 3–7% for AVIRIS Indian Pines scene, 2.5% for ROSIS Pavia University Area scene and 7–8% for AVIRIS Salinas scene. For this framework (Modified-CKV with MLR) we have obtained the overall accuracy of 96.95% for the Indian Pines scene, 99.33% for the Pavia University Area, and 97.76% for the Salinas scene with 75 training samples per class, which is less than 2% of the available labelled samples. With the results obtained, we have also concluded that with the linear combination approach for the construction of composite kernels, the overall accuracy improves without any increase in computational complexity.