AI FEATURE EXTRACTION FOR PRISMA HYPERSPECTRAL DATA

This paper introduces a hybrid approach to dimension reduction of PRISMA hyperspectral data, employing both linear and non-linear techniques: Principal Component Analysis (PCA) and autoencoders. The study aims to validate the efficacy of autoencoders by comparing results with the wellestablished PCA method. Our primary objective is to harness the complementary strengths of both methods in a hybrid framework, wherein certain bands may exhibit superior performance with autoencoders, while others fare better with PCA in dimension reduction. This strategic amalgamation not only accelerates data transfers and lowers computational costs for real-time applications but also leverages the specific advantages offered by each technique. The paper underscores the potential of this hybrid approach for optimizing hyperspectral data for enhanced feature extraction in various neural network applications.