Kernel Methods in Remote Sensing

This presentation introduces kernel-based machine learning techniques and their application to remote sensing data analysis. It outlines the challenges in remote sensing, such as high-dimensional data, noise, and limited ground truth, and explains how kernel methods like Support Vector Machines (SVM) can address these issues by mapping data into higher-dimensional feature spaces for effective classification and feature extraction. The presentation includes examples of urban vs. non-urban classification using satellite imagery, compares various kernel-based algorithms, and highlights the advantages of kernel methods over classical machine learning approaches in handling nonlinearity and improving model generalization.