Hybrid optimal joint spatial-spectral hyperspectral image classification using modified DHO-based GIF with JRKNN

Abstract

ABSTRACT Hyperspectral images (HSI) have been extensively utilized in environmental protection, agriculture, and land cover applications. But, it is difficult to classify due to improper exploitation of spatial and spectral features. As a result, this paper proposes a unified guided image filtering (GIF) method that uses a joint representation model of the K-nearest neighbour algorithm (JRKNN) to extract spatial context information and preserve edges while denoising classification results. In addition, a new meta-heuristic optimization algorithm called modified deer hunting optimization (MDHO) is implemented to minimize the cost function in GIF by optimizing the regularization parameter and edge-aware weighting, which is named GIF-MDHO. Finally, the simulation results show that the GIF-MDHO with JRKNN approach performed superior as compared to the existing support vector machine (SVM) by KNN and even the KNN-GIF methods with an improved accuracy of 1.02%, precision, recall , and F1-score of 0.78%, 0.91%, and 0.74%, respectively.