Improving SAR and Optical Image Fusion for Lulc Classification with Domain Knowledge

Fusing SAR and multi-spectral images to generate a precise land cover map in a weakly supervised setting is a challenging yet essential problem. The inaccurate, noisy, and inexact ground truth labels pose difficulty training any machine learning models. In this paper, we make a fundamental and pivotal contribution towards improving the ground truth label quality using domain knowledge. We present a simple yet effective mechanism to refine the low-resolution noisy ground truth labels. The proposed approach is trained and tested on a publicly available DFC2020 dataset. Through experiments, we show the effectiveness of our method by training a deep learning model on the refined labels that outperform even the models trained with clean ground truth.