PRISMA Hyperspectral Image Segmentation with U-Net Convolutional Neural Network Using Singular Value Decomposition for Mapping Mining Areas: Preliminary Results

This work is focused on a deep learning model–U-Net convolutional neural network–with the purpose of segmenting relevant imagery classes, for detecting mining areas using hyperspectral images of the PRISMA Earth Observation mission, funded by the Italian Space Agency (ASI). To avoid the typical problem of hyperspectral data redundancy and to improve the computational performances without losing accuracy, the Singular Value Decomposition (SVD) is applied to the hyperspectral data cube, taking only the first three singular values, thus projecting the multi-dimensional data cube to a three channels image. The method is applied to a PRISMA surface reflectance scene of South-West Sardinia, one of the oldest mining districts in the world. The Quadrilátero Ferrífero mining district (Minas Gerais, Brazil) will also be analyzed to test the transferability of the model to other mining areas worldwide.