Optimized Deep Learning Framework for Improved Semantic Segmentation in Multispectral Imagery

This paper investigates the application of deep learning techniques to enhance the semantic segmentation of multispectral images. This paper presents a optimized deep learning model tailored for high-dimensional data characteristic of multispectral imagery. The proposed model integrates convolutional, pooling, and upsampling layers designed to optimize feature extraction and accurate pixel classification across various landscapes. Comparative analysis through the detailed experimentation showcases the superiority of the proposed model in handling the complexity of multispectral data with greater accuracy and efficiency. Results from this study demonstrate significant improvements in segmentation precision, particularly in distinguishing between subtle differences in spectral signatures, which are essential for accurate land use classification and disaster management. The findings suggest that integrating advanced deep learning architectures can profoundly enhance the analysis capabilities of multispectral imagery, leading to better-informed environmental decisions.