Exploring High-Resolution Satellite Image Segmentation through Convolutional Neural Networks: A Comprehensive Analysis and Performance Evaluation

Abstract

Satellite image segmentation plays a pivotal role in extracting meaningful information from vast and intricate spatial datasets. This study delves into the application of Convolutional Neural Networks (CNNs) for the segmentation of high-resolution satellite images, aiming to enhance the precision and efficiency of geospatial analysis. The proposed approach leverages the capabilities of deep learning to automatically learn intricate patterns and spatial dependencies inherent in satellite imagery. This study investigates the application of Convolutional Neural Networks (CNNs) for high-resolution satellite image segmentation. Through a meticulous analysis, we assess the performance of various CNN architectures on diverse datasets, evaluating their effectiveness in delineating land cover classes. The experiments, encompassing rigorous training and testing phases, highlight the superiority of CNNs over traditional methods. Insights into transfer learning, data augmentation, and robustness considerations are provided. The findings underscore the potential of CNNs in advancing geospatial analysis and satellite image interpretation.