Abstract
Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper, we propose a generative adversarial network with spatial and channel attention mechanisms (GAN-SCA) for the robust segmentation of buildings in remote sensing images. The segmentation network (generator) of the proposed framework is composed of the well-known semantic segmentation architecture (U-Net) and the spatial and channel attention mechanisms (SCA). The adoption of SCA enables the segmentation network to selectively enhance more useful features in specific positions and channels and enables improved results closer to the ground truth. The discriminator is an adversarial network with channel attention mechanisms that can properly discriminate the outputs of the generator and the ground truth maps. The segmentation network and adversarial network are trained in an alternating fashion on the Inria aerial image labeling dataset and Massachusetts buildings dataset. Experimental results show that the proposed GAN-SCA achieves a higher score (the overall accuracy and intersection over the union of Inria aerial image labeling dataset are 96.61% and 77.75%, respectively, and the F 1 -measure of the Massachusetts buildings dataset is 96.36%) and outperforms several state-of-the-art approaches.
| Language | English |
|---|---|
| Article number | 917 |
| Number of pages | 18 |
| Journal | Remote Sensing |
| Volume | 11 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 15 Apr 2019 |
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Keywords
- deep learning
- generative adversarial network
- high-resolution aerial images
- inria aerial image labeling dataset
- Massachusetts buildings dataset
- semantic segmentation
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Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms. / Pan, Xuran; Yang, Fan; Gao, Lianru; Chen, Zhengchao; Zhang, Bing; Fan, Hairui; Ren, Jinchang.
In: Remote Sensing, Vol. 11, No. 8, 917, 15.04.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms
AU - Pan, Xuran
AU - Yang, Fan
AU - Gao, Lianru
AU - Chen, Zhengchao
AU - Zhang, Bing
AU - Fan, Hairui
AU - Ren, Jinchang
PY - 2019/4/15
Y1 - 2019/4/15
N2 - Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper, we propose a generative adversarial network with spatial and channel attention mechanisms (GAN-SCA) for the robust segmentation of buildings in remote sensing images. The segmentation network (generator) of the proposed framework is composed of the well-known semantic segmentation architecture (U-Net) and the spatial and channel attention mechanisms (SCA). The adoption of SCA enables the segmentation network to selectively enhance more useful features in specific positions and channels and enables improved results closer to the ground truth. The discriminator is an adversarial network with channel attention mechanisms that can properly discriminate the outputs of the generator and the ground truth maps. The segmentation network and adversarial network are trained in an alternating fashion on the Inria aerial image labeling dataset and Massachusetts buildings dataset. Experimental results show that the proposed GAN-SCA achieves a higher score (the overall accuracy and intersection over the union of Inria aerial image labeling dataset are 96.61% and 77.75%, respectively, and the F 1 -measure of the Massachusetts buildings dataset is 96.36%) and outperforms several state-of-the-art approaches.
AB - Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper, we propose a generative adversarial network with spatial and channel attention mechanisms (GAN-SCA) for the robust segmentation of buildings in remote sensing images. The segmentation network (generator) of the proposed framework is composed of the well-known semantic segmentation architecture (U-Net) and the spatial and channel attention mechanisms (SCA). The adoption of SCA enables the segmentation network to selectively enhance more useful features in specific positions and channels and enables improved results closer to the ground truth. The discriminator is an adversarial network with channel attention mechanisms that can properly discriminate the outputs of the generator and the ground truth maps. The segmentation network and adversarial network are trained in an alternating fashion on the Inria aerial image labeling dataset and Massachusetts buildings dataset. Experimental results show that the proposed GAN-SCA achieves a higher score (the overall accuracy and intersection over the union of Inria aerial image labeling dataset are 96.61% and 77.75%, respectively, and the F 1 -measure of the Massachusetts buildings dataset is 96.36%) and outperforms several state-of-the-art approaches.
KW - deep learning
KW - generative adversarial network
KW - high-resolution aerial images
KW - inria aerial image labeling dataset
KW - Massachusetts buildings dataset
KW - semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85065020057&partnerID=8YFLogxK
U2 - 10.3390/rs11080966
DO - 10.3390/rs11080966
M3 - Article
VL - 11
JO - Remote Sensing
T2 - Remote Sensing
JF - Remote Sensing
SN - 2072-4292
IS - 8
M1 - 917
ER -