CT-UNet : An Improved Neural Network Based on U-Net for Building Segmentation in Remote Sensing Images

Author / Creator

ICPR 2020 MAIN CONFERENCE (2021)

Conferences

ICPR 2020 MAIN CONFERENCE PS T5.3: Imaging and Deep Image Processing (2021)

Available as

Online

Summary

With the proliferation of remote sensing images, how to segment buildings more accurately in remote sensing images is a critical challenge. First, the high resolution leads to blurred boundaries in...

With the proliferation of remote sensing images, how to segment buildings more accurately in remote sensing images is a critical challenge. First, the high resolution leads to blurred boundaries in the extracted building maps. Second, the similarity between buildings and background results in intraclass inconsistency. To address these two problems, we propose an UNet-based network named Context-Transfer-UNet (CT-UNet). Specifically, we design Dense Boundary Block (DBB). Dense Block utilizes reuse mechanism to refine features and increase recognition capabilities. Boundary Block introduces the low-level spatial information to solve the fuzzy boundary problem. Then,to handle intra-class inconsistency, we construct Spatial Channel Attention Block (SCAB). Finally, we propose a novel loss function to enhance the purpose of loss by adding evaluation indicator.

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