Wavelet-Based Deep Generative Framework for Super Resolution of Low-Resolution Labeled Maps and Weak-Supervised Learning

The unavailability of pixel-level detailed labels is a crucial challenge in the field of remote sensing image analysis. Deep learning models require a large number of labeled samples for an accurate estimation of a large number of trainable parameters. However, in remote sensing applications usually only a few reliable labeled data are available for the learning of a classifier, whereas often many weak/low-resolution unreliable labeled data can be collected from available land-cover maps. Accordingly, weak supervised learning may overcome the problems by using noisy and low-resolution labels in remote sensing. In this paper, we propose a deep adversarial model based on discrete wavelet transform to exploit weak/low-resolution label information for generating refined super-resolved Weak Reference Maps (WRM). Our contribution includes the development of a discrete wavelet transform based generator for enhancing the low-resolution labels to generate a refined high-resolution reference map. We also present an efficient framework for multi-source image fusion that incorporates the refined super-resolved WRM, synthetic aperture radar images and corresponding low-resolution labels. Our findings highlight the effectiveness of the refined super-resolved WRM. Additionally, we investigate the impact of the high-resolution reference maps on segmentation accuracy, which reveals their potential in improving the segmentation performance compared to other reference methods.