An Improved Hunter-Prey Optimizer-Based DenseNet Model for Classification of Hyper-Spectral Images

Abstract

In this chapter, the authors offer an already-trained CNN model for HSI classification. By fine-tuning the parameters, the suggested DenseNet model classification accuracy is increased. In order to fine-tune the hyper-parameters, an enhanced version of the Hunter-Prey Optimisation algorithm (IHPOA) is used. The convergence of the HPO technique is sped up by the addition of adaptive inertia weights to the optimisation search phase. At the same time, the authors tweak the starting population to boost the procedure capacity to do worldwide searches. Extensive experimental findings collected on three publicly available HSI datasets show that the suggested technique may minimise computational complexity and over smoothing while maintaining competitive performance compared to numerous state-of-the-art approaches.