LAUSR

A precise and robust classification of land cover is crucial for land use estimation. A robust model that can provide rich semantic information is imperative for the challenging task of land cover classification (LCC) in foggy conditions. We propose semantic representation enhancement (SRE) and semantic representation aggregation (SRA) modules for the fusion of semantic representation. The dense depthwise separable atrous spatial pyramid pooling (DDS-ASPP) module in SRE possesses a large receptive field, which covers an extensive scale range. An enhanced asymmetric convolution module (EACM) in SRE focuses on features of various directions. DDS-ASPP and EACM generate the class-based and pixel-based representation, respectively. By means of SRA and dual representations, we model the relationship between global context and coarse class regions to capture long-range correlation. Moreover, evaluated on Potsdam, Vaihingen, and custom real-world datasets under fog, we demonstrate that our work is competitive with state-of-the-art models in terms of robustness. Code will be available at https://github.com/bowenroom/Robust-land-cover-classification.