Land Cover Mapping Based On Multi-Branch Fusion Of Object-Based And Pixel-Based Segmentation With Filtered Labels

In this paper, a multi-branch fusion framework is proposed to address the land cover mapping issue with low-resolution labels. To obtain homogeneous target objects, a multi-resolution segmentation (MRS) algorithm is applied to yield unsupervised object-based segmentation maps. Through an index-based judgement mechanism, a label filtering principle was designed and employed to screen out samples with noisy labels while retaining samples with clean labels, thus acquiring more accurate training data. A patch-to-point classification network was established based on these filtered training patches, which fully extracts the contextual features and generates pixel-based prediction results. A post-processing step, consisting of fusion and voting operations, was developed to merge the pixel-based and object-based results, and produce a final segmentation map. Verified through the competition website, the proposed method achieved an average accuracy (AA) of 57.22%, ranking second in the first track of 2020 IEEE GRSS Data Fusion Contest.