A hybrid spectral-spatial fusion technique for hyperspectral object classification

Abstract

In the field of object classification, hyperspectral imaging (HSI) has been widely used, due to its spectral-spatial, and temporal resolution of larger areas. The HSI is generally used to identify the objects physical properties in accurate manner and as well as to identify similar object with acceptable spectral signatures. Thus, the HSI has been widely used for object identification applications in different fields such as precision agriculture, environmental study, crop monitoring, and surveillance. However, the object classification is time consuming due to extremely large size; thus, the feature fusion of both spectral and spatial have been done. The current feature fusion method fails to retain semantic object intrinsic feature; further, current classification technique induces higher misclassification. In addressing the research issues this paper introduces a hybrid spectral-spatial fusion (HSSF) technique to reduce feature size and retains object intrinsic properties. Finally, in reducing misclassification a soft-margins kernel is introduced in support vector machine (SVM). Experiment is conducted on standard Indian Pines dataset; the result shows the HSSF-SVM model attain much higher accuracy and Kappa coefficient performance.