MISNet: Multiscale Cross-Layer Interactive and Similarity Refinement Network for Scene Parsing of Aerial Images

Abstract

Although progress has been made in multisource data scene parsing of natural scene images, extracting complex backgrounds from aerial images of various types and presenting the image at different scales remain challenging. Various factors in high-resolution aerial images (HRAIs), such as imaging blur, background clutter, object shadow, and high resolution, substantially reduce the integrity and accuracy of object segmentation. By applying multisource data fusion, as in scene parsing of natural scene images, we can solve the aforementioned problems through the integration of auxiliary data into HRAIs. To this end, we propose a multiscale cross-layer interactive and similarity refinement network (MISNet) for scene parsing of HRAIs. First, in a feature fusion optimization module, we extract, filter, and optimize multisource features and further guide and optimize the features using a feature guidance module. Second, a multiscale context aggregation module increases the receptive field, captures semantic information, and extracts rich multiscale background features. Third, a dense decoding module fuses the global guidance information and high-level fused features. We also propose a joint learning method based on feature similarity and a joint learning module to obtain deep multilevel information, enhance feature generation, and fuse multiscale and global features to enhance network representation for accurate scene parsing of HRAIs. Comprehensive experiments on two benchmark HRAIs datasets indicate that our proposed MISNet is qualitatively and quantitatively superior to similar state-of-the-art models.