This thesis focuses on the application of advanced semantic segmentation techniques to multispectral satellite images, primarily for the analysis and classification of woodlands. A key characteristic of this work is the use of limited datasets, both in terms of the number of images and the accuracy of the masks. The thesis begins with an introduction defining the problem of semantic segmentation and outlining the research objectives, which involve comparing various methodologies to evaluate their effectiveness in classification tasks. Following this, a literature review explains the principles of neural networks, deep learning models, and the various techniques currently in use, including the concepts of transfer learning and fine-tuning, which are crucial for understanding the work conducted.