Consecutive spatial-spectral framework for remote sensing image classification

This paper contributes an artificial intelligence classifier for hyperspectral images by simultaneously employing spectral and spatial information in kernel functions and using the stochastic relaxation of spectral and spatial energy. The spectral and spatial features are integrated with the Consecutive Spectral-Spatial/Spatial-Spectral (CS2) kernel which exhibits great flexibility in the combination of spectral and spatial mapping functions. The overall classification accuracy and individual class accuracies of the proposed classier were compared with some well-known spectral-spatial kernels in two empirical hyperspectral benchmark datasets. The experimental findings demonstrated the flexibility and effectivity of CS2 kernel in the field of simultaneous spectral-spatial processing. The computational complexity of the proposed CS2 kernel-based Support Vector Machine (CS3VM) is adequacy equal to the required amount of execution time of published approaches in spectral-spatial kernel literature. The advantage of the CS2 kernel is its generalization capability in spectral-spatial kernel machines. An additional post-processing step is exploited to preserve the edge of regions and to generate homogenized classification maps based on probabilistic weighted spectral-spatial energy minimization. Different types of evaluation metrics were used to prove the accuracy of the proposed framework under different training conditions and scenarios. The CS2 kernel machines produced very competitive results and the proposed probabilistic stochastic relaxation approach improved the average accuracy, overall accuracy and the kappa statistic with Indian Pines and University of Pavia hyperspectral datasets. The novel framework provides smooth enough homogeneous regions classification maps with sharp edges on the boundaries and meaningfully enhances the classification performance with limited training samples.