Semantic Segmentation of Crops via Hyperspectral PRISMA Satellite Images

Data from hyperspectral remote sensing are promising to extract and classify crop characteristics, because it provides accurate and continuous spectral signatures of crops. This paper focuses on data acquired by PRISMA, a high-resolution hyperspectral imaging satellite. Due to this large data availability, huge training datasets can be built to feed modern deep learning algorithms. This paper shows a spectral-temporal data processing based on random forest to perform feature selection, and on two-dimensional convolutional neural network to carry out classification of crops, exploiting variations in respective phenological phases during the annual life cycle. The proposed solution is described via a pilot case study, involving a field farmed with olive groves and vineyards in Apulia, Italy. Moreover, one-dimensional convolutional neural networks are used to compare classification accuracies. Early results are promising with respect to the literature.