Land Cover Classification from VHR Optical Remote Sensing Images by Feature Ensemble Deep Learning Network

Land cover classification is a popular research field in remote sensing applications, which have to both consider the pixel-level classification and boundary mapping comprehensively. Although multi-scale features in deep learning (DL) network have a powerful classification ability, how to use multi-scale feature description to produce an accurate land cover classification from very high resolution (VHR) optical remote sensing image is still a challenging task because of large intraclass or small interclass difference of land covers. Therefore, aiming at achieving more accurate pixel-level land cover classification, we proposed a novel feature ensemble network (FE-Net), which includes the multi-scale feature encapsulation and enhancement two phases. First, there are encapsulated shallow, middle, and deep scale feature layers from Resnet-101 backbone. Second, related to multi-scale feature description enhancement, these 2-D dilation convolutions with different sample rates are employed on each scale feature layer. After that, optimal channel selection works on each intrascale and interscale feature layers sequentially. Finally, extensive experiments proved that the proposed FE-Net combined with a special joint loss function outperforms state-of-the-art DL based methods. It can achieve the 68.08% and 65.16% of the mean of class-wise intersection over union (mIoU) on ISPRS and GID data sets, respectively.