期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 11, 期 10, 页码 3680-3687出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2018.2865187
关键词
Adversarial network; building extraction; fully convolutional DenseNet (FC-DenseNet); remote sensing; structural feature learning
类别
资金
- Jiangsu Province Geographic Information Research Project [JSCHKY201720]
- National Science technology Support Plan Project of China [2015BAJ02B00]
- China Scholarship Council [201704910704]
With the proliferation of high-resolution remote sensing sensor and platforms, vast amounts of aerial image data are becoming easily accessed. High-resolution aerial images provide sufficient structural and texture information for image recognition while also raise new challenges for existing segmentation methods. In recent years, deep neural networks have gained much attention in remote sensing field and achieved remarkable performance for high-resolution remote sensing images segmentation. However, there still exists spatial inconsistency problems caused by independently pixelwise classification while ignoring high-order regularities. In this paper, we developed a novel deep adversarial network, named Building-A-Nets, that jointly trains a deep convolutional neural network (generator) and an adversarial discriminator network for the robust segmentation of building rooftops in remote sensing images. More specifically, the generator produces pixelwise image classification map using a fully convolutional DenseNet model, whereas the discriminator tends to enforce forms of high-order structural features learned from ground-truth label map. The generator and discriminator compete with each other in an adversarial learning process until the equivalence point is reached to produce the optimal segmentation map of building objects. Meanwhile, a soft weight coefficient is adopted to balance the operation of the pixelwise classification and high-order structural feature learning. Experimental results show that our Building-A-Net can successfully detect and rectify spatial inconsistency on aerial images while archiving superior performances compared to other state-of-the-art building extraction methods.
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