Optimizing Hyperspectral Image Classification Through Advanced Deep Learning Models Enhanced by Adam

In modern urban planning, agricultural production, and environmental monitoring, classification tasks necessitate sophisticated approaches due to the inherent complexity of hyperspectral images (HSIs), characterized by their abundant spectral bands and consequent high dimensionality.Such profusion poses significant challenges for effective data processing, analysis, and classification.Addressing these challenges, the application of deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a pivotal advancement.By exploiting the inherent spectral correlation and spatial context, these networks are adept at extracting pertinent features from the high-dimensional data of HSIs, thereby significantly enhancing classification performance.This study introduces four novel deep learning models optimized with the Adam algorithm: a 3-Dimensional Convolutional Neural Network (3D-CNN), a 2-Dimensional Convolutional Neural Network (2D-CNN), a recurrent 3D-CNN (R-3D CNN), and a recurrent 2D-CNN (R-2D-CNN).The Adam optimizer, known for its adaptive nature and the utilization of moving averages of gradients and their squares, is demonstrated to efficiently handle sparse gradients, thus providing stability during the optimization process.Comprehensive analyses were conducted on two publicly available databases-Indian Pine and Pavia University-yielding notable results.The employment of the Adam optimizer facilitated the attainment of exceptional performance metrics, evidenced by a kappa coefficient of 99.6%, a processing time of 322.18 seconds, an overall accuracy of 99.97%, and an average accuracy of 97.6% for the Indian Pine dataset; similarly, for the Pavia University dataset, results showcased a kappa coefficient of 99.1%, a processing time of 293.74 seconds, an overall accuracy of 99.98%, and an average accuracy of 99.99%.These findings underscore the superiority of the proposed deep learning models, particularly the R-3D-CNN and R-2D-CNN, over traditional classification approaches.The study not only introduces a novel optimization technique for managing high-dimensional data but also provides a comparative analysis with conventional methods, affirming the exceptional capabilities of the advanced deep learning models in hyperspectral image classification.