Abstract

In this research, a novel model for multispectral image classification and analysis, leveraging Spectral-Spatial Deep DenseNet Learning is presented. This proposed framework combines spectral and spatial information to enhance the discriminative power of deep neural networks, enabling accurate classification of multispectral images. We conduct extensive experiments on benchmark datasets, demonstrating the superior performance of our method compared to existing approaches. Furthermore, we provide a comprehensive analysis of the learned features, shedding light on the interpretability and effectiveness of our model for multispectral image analysis tasks.