|
|
结合通道交互空间组注意力与金字塔池化的高分影像语义分割网络 |
汪超宇1,2,杜震洪1,2(),汪愿愿2,3 |
1.浙江大学 地球科学学院 地理信息科学研究所, 浙江 杭州 310058 2.浙江大学 浙江省资源与环境信息系统重点实验室,浙江 杭州 310028 3.浙江大学 海洋研究院,浙江 舟山 316021 |
|
High-resolution image semantic segmentation network combining channel interaction spatial group attention and pyramid pooling |
Chaoyu WANG1,2,Zhenhong DU1,2(),Yuanyuan WANG2,3 |
1.Department of Geographic Information Science,Zhejiang University,Hangzhou 310058,China 2.Zhejiang Provincial Key Lab of Geographic Information Science,Zhejiang University,Hangzhou 310028,China 3.Ocean Academy,Zhejiang University,Zhoushan 316021,Zhejiang Province,China |
1 |
徐佳伟, 刘伟, 单浩宇, 等. 基于PRCUnet的高分遥感影像建筑物提取[J]. 地球信息科学学报, 2021, 23(10): 1838-1849. doi:10.12082/dqxxkx.2021.210283 XU J W, LIU W, SHAN H Y, et al. High-resolution remote sensing image building extraction based on PRCUnet[J]. Journal of Geo-Information Science, 2021, 23(10): 1838-1849. doi:10.12082/dqxxkx.2021.210283
doi: 10.12082/dqxxkx.2021.210283
|
2 |
邵振峰, 孙悦鸣, 席江波, 等. 智能优化学习的高空间分辨率遥感影像语义分割[J]. 武汉大学学报(信息科学版), 2022, 47(2): 234-241. DOI:10.13203/j.whugis20200640 SHAO Z F, SUN Y M, XI J B, et al. Intelligent optimization learning for semantic segmentation of high spatial resolution remote sensing images[J]. Geomatics and Information Science of Wuhan University, 2022, 47(2): 234-241. DOI:10.13203/j.whugis20200640
doi: 10.13203/j.whugis20200640
|
3 |
张万福. 基于随机森林的图像语义分割算法的研究[J]. 电子科技, 2017, 30(2): 72-75. DOI:10.16180/j.cnki.issn1007-7820.2017.02.019 ZHANG W F. Semantic segmentation algorithm based on random forests[J]. Electronic Science and Technology, 2017, 30(2): 72-75. DOI:10.16180/j.cnki.issn1007-7820.2017.02.019
doi: 10.16180/j.cnki.issn1007-7820.2017.02.019
|
4 |
潘欣欣. 基于各向异性马尔科夫随机场的遥感影像分割[D]. 开封: 河南大学, 2020. DOI:10.27114/d.cnki.ghnau.2020.000266 PAN X X. Remote Sensing Image Segmentation based on Anisotropic Markov Random Field[D]. Kaifeng: Henan University, 2020. DOI:10.27114/d.cnki.ghnau.2020.000266
doi: 10.27114/d.cnki.ghnau.2020.000266
|
5 |
袁正午, 朱冠宇, 丰江帆, 等. 基于支持向量机的视频语义场景分割算法研究[J]. 重庆邮电大学学报(自然科学版), 2010, 22(4): 458-463. YUAN Z W, ZHU G Y, FENG J F, et al. Research on the method of video semantic scene constructing based on SVM[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2010, 22(4): 458-463.
|
6 |
LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]// 2015 IEEE Conference on Computer Vision and Patter-n Recognition (CVPR). Boston: IEEE, 2015: 3431-3440. DOI:10.1109/CVPR.2015.7298965
doi: 10.1109/CVPR.2015.7298965
|
7 |
RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segme-ntation[C]// 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer, 2015: 234-241. DOI:10.1007/978-3-319-24574-4_28
doi: 10.1007/978-3-319-24574-4_28
|
8 |
ZHOU Z W, SIDDIQUEE M M R, TAJBAKHSH N, et al. UNet++: Redesigning skip connections to exploit multiscale features in image segmentation[J]. IEEE Transactions on Medical Imaging,2020, 39(6): 1856-1867. DOI:10.1109/TMI.2019.2959609 .
doi: 10.1109/TMI.2019.2959609
|
9 |
李传林, 黄风华, 胡威, 等. 基于Res_AttentionUnet的高分辨率遥感影像建筑物提取方法[J]. 地球信息科学学报, 2021, 23(12): 2232-2243. DOI:10.12082/dqxxkx.2021.210008 LI C L, HUANG F H, HU W, et al. Buildingextraction from high-resolution remote sensing image based on Res_AttentionUnet[J]. Journal of Geo-Information Science, 2021, 23(12): 2232-2243. DOI:10.12082/dqxxkx.2021. 210008
doi: 10.12082/dqxxkx.2021. 210008
|
10 |
王振庆,周艺,王世新,等. IEU-Net高分辨率遥感影像房屋建筑物提取[J]. 遥感学报, 2021, 25(11): 2245-2254. DOI:10.11834/jrs.20210042 WANG Z Q, ZHOU Y, WANG S X,et al. House building extraction from high-resolution remote sensing images based on IEU-Net[J]. National Remote Sensing Bulletin, 2021, 25(11): 2245-2254. DOI:10.11834/jrs.20210042
doi: 10.11834/jrs.20210042
|
11 |
董子意, 杜震洪, 吴森森, 等. 基于改进U-Net网络的海洋中尺度涡自动检测模型[J]. 海洋学报, 2022, 44(2): 123-131. DOI:10.12284/hyxb2022038 DONG Z Y, DU Z H, WU S S, et al. An automatic marine mesoscale eddy detection model based on improved U-Net network[J]. Acta Oceanologica Sinica, 2022, 44(2): 123-131. DOI:10.12284/hyxb2022038
doi: 10.12284/hyxb2022038
|
12 |
李鑫伟, 李彦胜, 张永军. 弱监督深度语义分割网络的多源遥感影像水体检测[J]. 中国图象图形学报, 2021, 26(12): 3015-3026. DOI:10.11834/jig.200192 LI X W, LI Y S, ZHANG Y J. Weakly supervised deep semantic segmentation network for water body extraction based on multisource remote sensing imagery[J]. Journal of Image and Graphics, 2021, 26(12): 3015-3026. DOI:10.11834/jig.200192
doi: 10.11834/jig.200192
|
13 |
QI L, YAO Y, DAVID E E, et al. Remote sensing of brine shrimp cysts in Salt Lakes[J]. Remote Sensing of Environment, 2021, 266: 112695. DOI:10.1016/j.rse.2021.112695
doi: 10.1016/j.rse.2021.112695
|
14 |
杨佳林, 郭学俊, 陈泽华. 改进U-Net型网络的遥感图像道路提取[J]. 中国图象图形学报, 2021, 26(12): 3005-3014. DOI:10.11834/jig.200579 YANG J L, GUO X J, CHEN Z H. Road extraction method from remote sensing images based on improved U-Net network[J]. Journal of Image and Graphics, 2021, 26(12): 3005-3014. DOI:10.11834/jig.200579
doi: 10.11834/jig.200579
|
15 |
HE K M, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]// Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778. DOI:10.1109/CVPR.2016.90
doi: 10.1109/CVPR.2016.90
|
16 |
侯向丹, 赵一浩, 刘洪普, 等. 融合残差注意力机制的UNet视盘分割[J]. 中国图象图形学报, 2020, 25(9): 1915-1929. DOI:10.11834/jig.190527 HOU X D, ZHAO Y H, LIU H P, et al. Optic disk segmentation by combining UNet and residual attention mechanism[J]. Journal of Image and Graphics, 2020, 25(9): 1915-1929. DOI:10.11834/jig.190527
doi: 10.11834/jig.190527
|
17 |
SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: A simple way to prevent neural networks from over fitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.
|
18 |
CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]// European Conference on Computer Vision. Munich: ECCV/NewYork: Springer, 2018: 833-851. DOI:10.1007/978-3-030-01234-2_49
doi: 10.1007/978-3-030-01234-2_49
|
19 |
何红术, 黄晓霞, 李红旮, 等. 基于改进U-Net网络的高分遥感影像水体提取[J]. 地球信息科学学报, 2020, 22(10): 2010-2022. DOI:10.12082/dqxxkx. 2020.190622 HE H S, HUANG X X, LI H G, et al. Water body extraction of high-resolution remote sensing image based on improved U-Net network [J]. Journal of Geo-Information Science, 2020, 22(10): 2010-2022. DOI:10.12082/dqxxkx.2020.190622
doi: 10.12082/dqxxkx.2020.190622
|
20 |
WOO S, PARK J, LEE J, et al. CBAM: Convolutional block attention module[C]//European Conference on Computer Vision. Munich: ECCV, 2018: 3-19. DOI:10.1007/978-3-030-01234-2_1
doi: 10.1007/978-3-030-01234-2_1
|
21 |
WANG Q L, WU B G, ZHU P F, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 11531-11539. DOI:10.1109/CVPR42600.2020.01155 .
doi: 10.1109/CVPR42600.2020.01155
|
22 |
WU Y, HE K. Group normalization[J]. International Journal of Computer Vision, 2020, 128: 742-755. DOI:10.1007/s11263-019-01198-w
doi: 10.1007/s11263-019-01198-w
|
23 |
ZHOU B, KHOSLA A, LAPEDRIZA A, et al. Object detectors emerge in deep scene CNNs[J]. Computer Science, 2014. DOI:10.48550/arXiv. 1412.6856
doi: 10.48550/arXiv. 1412.6856
|
24 |
HE K M, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916. DOI:10.1109/TPAMI.2015.2389824
doi: 10.1109/TPAMI.2015.2389824
|
25 |
ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 6230-6239. DOI:10.1109/CVPR. 2017.660 .
doi: 10.1109/CVPR. 2017.660
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|