Segmentation Sémantique des Images Satellitaires pour la Surveillance Environnementale

Abstract

This project focuses on the semantic segmentation of satellite images using deep lear- ning architectures. The task involves assigning a label to each pixel in an image to automa- tically identify the different elements present in a scene (bare soil, vegetation, urban areas,

water bodies, etc.). Following an in-depth study of classical segmentation approaches and deep learning principles, several architectures were implemented and compared. Experiments conducted on an annotated satellite image dataset enabled the evaluation of each model’s performance using metrics such as accuracy, Dice coefficient, and confusion matrix analysis. The results show that models integrating pre-trained backbones provide finer and more generalizable segmentation, especially in visually heterogeneous areas. This work highlights the growing importance of artificial intelligence in the processing of remote sensing data and paves the way for automated decision-support systems in critical fields such as environmental monitoring, security, and risk management.