Graph-Based Semi-Supervised Learning With Tensor Embeddings for Hyperspectral Data Classification

Hyperspectral data classification is one of the fundamental problems in remote sensing. Several algorithms based on supervised machine learning have been proposed to address it. The performance, however, of the proposed algorithms is inherently dependent on the amount and quality of annotated data. Due to recent advances in hyperspectral imaging and autonomous (unmanned) aerial vehicles collecting new hyperspectral data is an easy task. Annotating those data, however, is a tedious, time-consuming and costly task requiring the in-situ presence of human experts. One way to loosen the requirement of a large number of annotated data is the shift to semi-supervised learning combined with highly sample-efficient tensor-based neural networks. This study provides a comprehensive experimental analysis of the performance of a variety of graph-based semi-supervised learning techniques combined with tensor-based neural network embeddings for the problem of hyperspectral data classification. Experimental results suggest that the combination of tensor-based neural network embeddings with graph-based semi-supervised learning can significantly improve the classification results minimizing human annotation effort.