Refining Land-Cover Weak Labels Using a Discrete Wavelet Transform Inspired Deep Adversarial Model

Finding pixel-level information is a fundamental problem in computer vision and in remote sensing image analysis. Usually a classifier based on a deep learning model requires 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 absolute land cover maps. Accordingly, weak supervised learning may overcome the problems by utilising 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 feature maps. Our method mainly includes a feature learning generative network based on wavelet features and a multiscale modeling capability.