  Semantic labeling for very high resolution (VHR) images in urban areas, is of
significant importance in a wide range of remote sensing applications. However,
many confusing manmade objects and intricate fine-structured objects make it
very difficult to obtain both coherent and accurate labeling results. For this
challenging task, we propose a novel deep model with convolutional neural
networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet).
Specifically, for confusing manmade objects, ScasNet improves the labeling
coherence with sequential global-to-local contexts aggregation. Technically,
multi-scale contexts are captured on the output of a CNN encoder, and then they
are successively aggregated in a self-cascaded manner. Meanwhile, for
fine-structured objects, ScasNet boosts the labeling accuracy with a
coarse-to-fine refinement strategy. It progressively refines the target objects
using the low-level features learned by CNN's shallow layers. In addition, to
correct the latent fitting residual caused by multi-feature fusion inside
ScasNet, a dedicated residual correction scheme is proposed. It greatly
improves the effectiveness of ScasNet. Extensive experimental results on three
public datasets, including two challenging benchmarks, show that ScasNet
achieves the state-of-the-art performance.
