Evaluation of Unsupervised Deep Clustering Methods for Crop Classification Using SAR Image Sequences

Abstract

Reliable crop mapping is an essential tool for agricultural monitoring and food security. In the tropics, where cloud cover massively affects optical imagery, synthetic-aperture radar (SAR) imagery emerged as a cost-effective alternative for discriminating crops in large scale agricultural regions. Recently, unsupervised deep clustering approaches have emerged as a competitive alternative in several different applications with labeling restrictions. This paper explores this literature and evaluates the feasibility of such methods applied to crop classification in a tropical region from multi-temporal SAR image sequences. We focus on the k-Means-related deep clustering methods, specifically, on Deep Embedding Clustering and Deep K-Means. We report experiments conducted on a public dataset from a tropical region with highly complex crop dynamics. In our experiments the tested unsupervised approaches managed to deliver nearly 78% of supervised counterparts for this task <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The source codes are available at https:/github.com/DLoboT/Project_DL_2020.