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Context-aware cascade network for semantic labeling in VHR image

Citation Author(s):
Yongcheng Liu, Bin Fan, Lingfeng Wang, Jun Bai, Shiming Xiang, Chunhong Pan
Submitted by:
Yongcheng Liu
Last updated:
15 September 2017 - 9:54am
Document Type:
Presentation Slides
Document Year:
2017
Event:
Presenters:
Yongcheng Liu
Paper Code:
ICIP1701
 

Semantic labeling for the very high resolution (VHR) image of urban areas is challenging, because of many complex man-made objects with different materials and fine-structured ob-jects located together. Under the framework of convolutional neural networks (CNNs), this paper proposes a novel end-to-end network for semantic labeling. Specifically, our network not only improves the labeling accuracy of complex manmade objects by aggregating multiple context semantics with a cas-caded architecture, but also refines fine-structured objects by utilizing the low-level detail in shallow layers of CNNs with a hierarchical pyramid structure. Throughout the network, a dedicated residual correction scheme is employed to amend the latent fitting residual. As a result of these specific compo-nents, the whole model works in a global-to-local and coarse-to-fine manner. Experimental results show that our network outperforms the state-of-the-art methods on the large-scale ISPRS Vaihingen 2D Semantic Labeling Challengedataset.

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