Crop Type Mapping Using Prisma Hyperspectral Images and One-Dimensional Convolutional Neural Network

Over the last few years, crop type mapping has gained importance in remote sensing as it represents one of the most challenging problems in this field. Precise and continuous spectral signatures can significantly help to obtain a unambiguous distinction among the types of crop. This paper presents a discussion about the application of different types of hyperspectral imagery to crop-type mapping. This project is part of a collaboration among the Italian Space Agency (ASI), the Φ-lab in the ESRIN centre of the European Space Agency (ESA), and the Italian National Research Council (CNR). This works is mainly focused on the analysis of the PRISMA hyperspectral images, comparing them to airborn imagery from the Compact Airborne Spectrographic Imager (CASI) and short-wave infrared (SWIR) Airborne Spectrographic Imager (SASI). The continuous spectral signature over the SWIR and the visible and near-infrared (VNIR) channels will be used to perform a binary classification by means of a one-dimensional convolutional neural network. The test case with 3 tomato fields and 4 corn fields is sited near Gros-seto, in Tuscany, Italy. Results will show the potentialities offered by the PRISMA mission for remote sensing applications.