Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning in Remote Sensing

The development of deep learning based image representation learning (IRL) methods has attracted great attention in the context of remote sensing (RS) image understanding. Most of these methods require the availability of a high quantity and quality of annotated training images, which can be time-consuming and costly to gather. To reduce labeling costs, publicly available thematic maps, automatic labeling procedures or crowdsourced data can be used. However, such approaches increase the risk of including label noise in training data. It may result in overfitting on noisy labels when discriminative reasoning is employed as in most of the existing methods. This leads to sub-optimal learning procedures, and thus inaccurate characterization of RS images. In this paper, as a first time in RS, we introduce a generative reasoning integrated label noise robust representation learning (GRID) approach. GRID aims to model the complementary characteristics of discriminative and generative reasoning for IRL under noisy labels. To this end, we first integrate generative reasoning into discriminative reasoning through a variational autoencoder. This allows our approach to automatically detect training samples with noisy labels. Then, through our label noise robust hybrid representation learning strategy, GRID adjusts the whole learning procedure for IRL of these samples through generative reasoning and that of the other samples through discriminative reasoning. Our approach learns discriminative image representations while preventing interference of noisy labels during training independently from the IRL method. Thus, unlike the existing methods, GRID does not depend on the type of annotation, label noise, neural network, loss or learning task, and thus can be utilized for various RS image understanding problems. Experimental results show the effectiveness of GRID compared to state-of-the-art methods.