Deep Hierarchical Pyramid Network With High- Frequency -Aware Differential Architecture for Super-Resolution Mapping
Da He, Yanfei Zhong
Abstract
Super-resolution mapping (SRM) is a way to solve the mixed-pixel problem in urban land use/land cover caused by the limited spatial-resolving ability of satellite sensors, through resolution enhancement of the classification map. Recently, deep learning-based super-resolution mapping (DLSM) networks have been boomed, which can automatically learn a mapping pattern from low-resolution (LR) image to high-resolution (HR) land cover distribution to alleviate mixed-pixel problem. However, the urban compositions like buildings, trees, and roads exhibit a multiscale distribution with different size or orientation, which makes the traditional single-scale DLSM failed for an appropriate recognition. In addition, the urban compositions also show significant spatial heterogeneity with irregular distribution and intricate morphological shape, which are difficult to learn by simple convolutional layer. Therefore, it is necessary to explore the cue of these distribution characteristic to constrain the learning behavior of the network for better detail restoration. In this article, a deep hierarchical pyramid sub-pixel mapping network (HiSMNet) with high-frequency-aware differential architecture is proposed, which establishes an HP architecture to achieve explicit multiscale supervision of the feature map and prompt the network to learn a multiscale representation. In addition, a differential architecture is designed to enforce the network to intensify the learning of the high-frequency details. The validation experiments demonstrate that HiSMNet achieves superior performances in detailed delineation and outperformed the state-of-the-art DLSM models by up to 10% in terms of overall accuracy.