Multi-view Learning for Impervious Surface Mapping using High-resolution Multi-spectral Imagery and LiDAR Data

The use of multi-source remote sensing data to obtain urban impervious surface has become a popular research topic. Multi-source remote sensing data fusion techniques can provide object interpretation with a higher accuracy. However, most decision-level fusion methods make insufficient use of the complementary information and degree of association between similar object data. To fill this gap, in this paper, we propose a dual-view learning fusion classification method (DvLF) based on multi-view learning. First, DvLF uses co-training algorithm to combine multiple data sources for accurate classification, extracting easy-to-classify area while separating difficult-to-classify regions for further analysis. Secondly, a canonical correlation analysis method is adopted to mine the degree of association of similar object data for constructing a subspace projection field of each object sample. The data in the difficult-to-classify regions are classified in the projection field of each object, and then the results of each classification are fused by voting. Finally, the classification results of the two regions are combined into the classification results of the whole image to achieve impervious surface mapping. The proposed method is applied to the dual-sensor (high-resolution image and LiDAR) Buffalo dataset and the dual-sensor (RGB and multispectral LiDAR) Houston dataset. The experimental results show that our method achieved a significant improvement in classification accuracy compared to other methods. The overall classification accuracy of this new DvLF fusion method on the Buffalo and Houston datasets is 83.35% and 88.84%, respectively leading to accurate high-resolution impervious surface mapping.