GLCM-ResNet: Deep Neural Model for Noise Removal and Multiband Image Classification

Abstract

Hyperspectral image classification plays a crucial role in various remote sensing applications, requiring deep learning models that offer both high accuracy and stability. In this study, we propose “CGLM-tweaked ResNet-16”, an optimized variant of ResNet-16, demonstrating superior performance across hyperspectral datasets. Our experiments on the “Indian Pines” and “Pavia University” datasets reveal that, CGLM ResNet-16 outperforms standard ResNet-16, particularly in terms of accuracy and loss reduction. For the Indian Pines dataset, CGLM ResNet-16 achieves an impressive 99.88% accuracy with the lowest loss of 2.8%, surpassing other competing models. Similarly, for the Pavia University dataset, the model maintains low loss 0.23% while achieving competitive accuracy, signifying improved efficiency and model stability. The reduced loss values indicate better generalization and robustness, crucial for real-world applications. While the pro-posed model enhances classification performance, certain challenges persist, particularly in noise reduction across multiple layers. Future research should explore hybrid deep learning architectures to further optimize accuracy without increasing computational overhead. The biggest challenge ahead is cross domain analysis which remains a critical bottleneck in multiband image processing. Effective noise removal techniques tailored for hyperspectral imaging must be developed to enhance the model’s generalization across diverse datasets. Addressing these challenges will significantly improve real-world applications, such as remote sensing, land cover classification, and environmental monitoring.  In conclusion, CGLM ResNet-16 presents a promising statistical analysis method advancement in hyperspectral image classifi-cation, offering improved accuracy and loss minimization.