Soil Analysis with Very Few Labels Using Semi-Supervised Hyperspectral Image Classification

Current technological advancements bring exciting opportunities in hyperspectral image analysis across various Earth observation applications, with precision agriculture being one of their notable examples. However, gathering high-quality and representative ground-truth data for training supervised machine learners is extremely challenging in emerging use cases, as it is cost-inefficient and user-dependent. Thus, building (deep) machine learning models from very few training samples is of paramount practical importance. To tackle this issue, we propose a semi-supervised learning pipeline for elaborating deep learning models for multi-class hyperspectral image classification, while benefiting from the available unlabeled samples, leveraging pseudo-labeling and consistency regularization. Although our technique is model-agnostic, we exploit a deep residual network for classifying hyperspectral images according to the magnesium content in the imaged soil. The experimental study performed over a real-world dataset (HYPERVIEW) encompassing environmental tasks crucial to food sustainability showed that the proposed technique significantly outperforms the pre-trained models fine-tuned in a fully supervised way.