MANet: Multi-Scale Aware-Relation Network for Semantic Segmentation in Aerial Scenes

Semantic segmentation is an important yet unsolved problem in aerial scenes understanding. One of the major challenges is the intense variations of scenes and object scales. In this article, we propose a novel multi-scale aware-relation network (MANet) to tackle this problem in remote sensing. Inspired by the process of human perception of multi-scale (MS) information, we explore discriminative and diverse MS representations. For discriminative MS representations, we propose an inter-class and intra-class region refinement (IIRR) method to reduce feature redundancy caused by fusion. IIRR utilizes the refinement maps with intra-class and inter-class scale variation to guide MS fine-grained features. Then, we propose multi-scale collaborative learning (MCL) to enhance the diversity of MS feature representations. The MCL constrains the diversity of MS feature network parameters to obtain diverse information. Also, the segmentation results are rectified according to the dispersion of the multilevel network predictions. In this way, MANet can learn MS features by collaboratively exploiting the correlation among different scales. Extensive experiments on image and video datasets, which have large-scale variations, have demonstrated the effectiveness of our proposed MANet.