225
Views
0
CrossRef citations to date
0
Altmetric
Research Article

An unsupervised semantic segmentation method that combines the ImSE-Net model with SLICm superpixel optimization

, , , &
Article: 2341970 | Received 21 Jul 2023, Accepted 07 Apr 2024, Published online: 16 Apr 2024

References

  • Achanta, Radhakrishna, Appu Shaji, Kevin Smith, Aurelie Lucchi, and Pascal Fua. 2012. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods.” IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11): 2274–2281. https://doi.org/10.1109/TPAMI.2012.120.
  • Ahmadi, Salman, M. J. Valadan Zoej, Hamid Ebadi, Hamid Abrishami Moghaddam, and Ali Mohammadzadeh. 2010. “Automatic Urban Building Boundary Extraction from High Resolution Aerial Images Using an Innovative Model of Active Contours.” International Journal of Applied Earth Observation and Geoinformation 12 (3): 150–157. https://doi.org/10.1016/j.jag.2010.02.001.
  • Alam, Muhammad, Jian-Feng Wang, Cong Guangpei, L. V. Yunrong, and Yuanfang Chen. 2021. “Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images.” Mobile Networks and Applications 26 (1): 200–215. https://doi.org/10.1007/s11036-020-01703-3.
  • Baatz, M., and A. Schape. 2000. “Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation.” Proceedings of the Beiträge zum AGIT-Symposium, 12–23.
  • Caesar, H., J. Uijlings, and V. Ferrari. 2018. “COCO-Stuff: Thing and Stuff Classes in Context.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1209–1218.
  • Chen, Yanlin, Guojin He, Ranyu Yin, Kaiyuan Zheng, and Guizhou Wang. 2022. “Comparative Study of Marine Ranching Recognition in Multi-Temporal High-Resolution Remote Sensing Images Based on DeepLab-V3+ and U-Net.” Remote Sensing 14 (22): 5654. https://doi.org/10.3390/rs14225654.
  • Chen, Liangchieh, Yukun Zhu, George Papandreou, Florian Schrof, and Hartwig Adam. 2018. “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” Proceedings of the European Conference on Computer Vision, 801–818.
  • Cho, Jang Hyun, Utkarsh Mall, Kavita Bala, and Bharath Hariharan. 2021. “PiCIE: Unsupervised Semantic Segmentation Using Invariance and Equivariance in Clustering.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 16794–16804.
  • Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, and Neil Houlsby. 2020. “An Image Is Worth 16(16 Words: Transformers for Image Recognition at Scale.” Proceedings of the International Conference on Learning Representations.
  • Du, Shouji, Shihong Du, Bo Liu, and Xiuyuan Zhang. 2021. “Incorporating DeepLabv3+ and Object-Based Image Analysis for Semantic Segmentation of Very High Resolution Remote Sensing Images.” International Journal of Digital Earth 14 (3): 357–378. https://doi.org/10.1080/17538947.2020.1831087.
  • Feng, Qiying, Long Chen, C. L. Philip Chen, and Li Guo. 2020. “Deep Fuzzy Clustering – A Representation Learning Approach.” IEEE Transactions on Fuzzy Systems 28 (7): 1420–1433. https://doi.org/10.1109/TFUZZ.2020.2966173.
  • Gadde, Raghudeep, Varun Jampani, Martin Kiefel, Daniel Kappler, and Peter V. Gehler. 2015. Superpixel Convolutional Networks Using Bilateral Inceptions. Cham: Springer.
  • Guo, Z. C., J. M. Xu, and A. D. Liu. 2021. “Remote Sensing Image Semantic Segmentation Method Based on Improved Deeplabv3+.” Proceedings of the International Conference on Image Processing and Intelligent Control 11928:101–109.
  • Hamilton, Mark, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, and William T. Freeman. 2022. “Unsupervised Semantic Segmentation by Distilling Feature Correspondences.” Proceedings of the International Conference on Learning Representations.
  • Hou, Mengjing, Jianpeng Yin, Jing Ge, Yuanchuan Li, Qisheng Feng, and Tiangang Liang. 2020. “Land Cover Remote Sensing Classification Method of Alpine Wetland Region Based on Random Forest Algorithm.” Transactions Chin. Soc. Agric. Mach 51 (7): 220–227. https://doi.org/10.6041/j.issn.1000-1298.2020.07.025.
  • Huang, Xin., Y. X. Cao, and J. Y. Li. 2020. “An Automatic Change Detection Method for Monitoring Newly Constructed Building Areas Using Time-Series Multi-View High-Resolution Optical Satellite Images.” Remote Sensing of Environment 244: 111802. https://doi.org/10.1016/j.rse.2020.111802.
  • Jia, Xiaohong, Tao Lei, Peng Liu, Dinghua Xue, Hongying Meng, and Asoke K. Nandi. 2020. “Fast and Automatic Image Segmentation Using Superpixel-Based Graph Clustering.” IEEE Access 8: 211526–211539. https://doi.org/10.1109/ACCESS.2020.3039742.
  • Jiang, Feng, Qign Gu, Huizhen Hao, Na Li, YanWen Guo, and Daoxu Chen. 2016. “Survey on Content-Based Image Segmentation Methods.” Journal of Software 28 (1): 160–183. https://doi.org/10.13328/j.cnki.jos.005136.
  • Jiang, Jie, Chengjin Lyu, Siying Liu, Yongqiang He, and Xuetao Hao. 2020. “RWSNet: A Semantic Segmentation Network Based on SegNet Combined with Random Walk for Remote Sensing.” International Journal of Remote Sensing 41 (2): 487–505. https://doi.org/10.1080/01431161.2019.1643937.
  • Kotaridis, Ioannis., and Maria Lazaridou. 2021. “Remote Sensing Image Segmentation Advances: A Meta-Analysis.” ISPRS Journal of Photogrammetry and Remote Sensing 173: 309–322. https://doi.org/10.1016/j.isprsjprs.2021.01.020.
  • Kwak, S., S. Hong, and B. Han. 2017. “Weakly Supervised Semantic Segmentation Using Superpixel Pooling Network.” Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI’17), 4111–4117.
  • Lei, Tao, Yuntong Li, Wenzhen Zhou, Qibin Yuan, Chengbin Wang, and Xiaohong Zhang. 2022. “Grain Segmentation of Ceramic Materials Using Data-driven Jointing Model-driven.” Acta automatica sinica 48 (4): 1137–1152. https://doi.org/10.16383/j.aas.c200277.
  • Lei, Tao, Peng Liu, Xiaohong Jia, Xuande Zhang, Hongying Meng, and Asoke K. Nandi. 2020. “Automatic Fuzzy Clustering Framework for Image Segmentation.” IEEE Transactions on Fuzzy Systems 28 (9): 2078–2092. https://doi.org/10.1109/TFUZZ.2019.2930030.
  • Li, J. Y., X. Huang, and J. Y. Gong. 2019. “Deep Neural Network for Remote-Sensing Image Interpretation: Status and Perspectives.” National Science Review 6 (6): 1082–1086. https://doi.org/10.1093/nsr/nwz058.
  • Li, Haifeng, Kaijian Qiu, Li Chen, Xiaoming Mei, Liang Hong, and Chao Tao. 2021. “SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images.” IEEE Geoscience and Remote Sensing Letters 18 (5): 905–909. https://doi.org/10.1109/LGRS.2020.2988294.
  • Liu, Chun, Xin Huang, Zhe Zhu, Huijun Chen, Xinming Tang, and Jianya Gong. 2019a. “Automatic Extraction of Built-up Area from ZY3 Multi-View Satellite Imagery: Analysis of 45 Global Cities.” Remote Sensing of Environment 226 (June): 51–73. https://doi.org/10.1016/j.rse.2019.03.033.
  • Liu, Han, Jun Li, Lin He, and Yu Wang. 2019b. “Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification.” Remote Sensing 11 (2): 174. https://doi.org/10.3390/rs11020174.
  • Liu, W., A. Rabinovich, and A. C. Berg. 2015. “ParseNet: Looking Wider to See Better.” Proceedings of the International Conference on Learning Representations.
  • Ma, Bifang., and Chih-Yung Chang. 2022. “Semantic Segmentation of High-Resolution Remote Sensing Images Using Multiscale Skip Connection Network.” IEEE Sensors Journal 22 (4): 3745–3755. https://doi.org/10.1109/JSEN.2021.3139629.
  • Pedregosa, Fabian, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, and Bertrand Thirion. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12 (2011): 2825–2830. https://doi.org/10.48550/arXiv.1201.0490.
  • Ren, Xiaofeng., and Jitendra Malik. 2003. “Learning a Classification Model for Segmentation.” Proceedings Ninth IEEE International Conference on Computer Vision 1:10–17.
  • Ren, Z. L., Q. P. Zhai, and L. Sun. 2023. “Spectral Clustering Eigenvector Selection of Hyperspectral Image Based on the Coincidence Degree of Data Distribution.” International Journal of Digital Earth 16 (1): 3489–3512. https://doi.org/10.1080/17538947.2023.2251436.
  • Shao, Zhenfeng, Yueming Sun, Jiangbo Xi, and Yan Li. 2022. “Intelligent Optimization Learning for Semantic Segmentation of High Spatial Resolution Remote Sensing lmages.” Geomatics and Information Science of Wuhan University 47 (2): 234–241. https://doi.org/10.13203/j.whugis20200640.
  • Shelhamer, E., J. Long, and T. Darrell. 2017. “Fully Convolutional Networks for Semantic Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (4): 640–651. https://doi.org/10.1109/TPAMI.2016.2572683.
  • Su, Jingsuan, Liangxin Fan, Zhanliang Yuan, Zhen Wang, and Zhijun Wang. 2023. “Quantifying the drought sensitivity of grassland under different climate zones in Northwest China.” Science of the Total Environment 910: 168688. https://doi.org/10.1016/j.scitotenv.2023.168688.
  • Sun, Weiwei., and Ruisheng Wang. 2018. “Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images Combined With DSM.” IEEE Geoscience and Remote Sensing Letters 15 (3): 474–478. https://doi.org/10.1109/LGRS.2018.2795531.
  • Wang, Yiqin. 2021a. “Remote Sensing Image Semantic Segmentation Algorithm Based on Improved ENet Network.” Scientific Programming, Hindawi: e5078731. https://doi.org/10.1155/2021/5078731.
  • Wang, Zhen, Jianxin Guo, Wenzhun Huang, and Shanwen Zhang. 2021b. “High-Resolution Remote Sensing Image Semantic Segmentation Based on a Deep Feature Aggregation Network.” Measurement Science and Technology 32 (9): IOP Publishing: 095002. https://doi.org/10.1088/1361-6501/abfbfd.
  • Wang, Zhen, Zhaoqing Li, Rong Wang, Feiping Nie, and Xuelong Li. 2021c. “Large Graph Clustering With Simultaneous Spectral Embedding and Discretization.” IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (12): 4426–4440. https://doi.org/10.1109/TPAMI.2020.3002587.
  • Wang, Zhipan, Zhongwu Wang, Dongmei Yan, Zewen Mo, and Hua Zhang. 2023. “RepDDNet: A Fast and Accurate Deforestation Detection Model with High-Resolution Remote Sensing Image.” International Journal of Digital Earth 16 (1): 2013–2033. https://doi.org/10.1080/17538947.2023.2220619.
  • Wang, Zhimin, Jiasheng Wang, Kun Yang, Limeng Wang, and Fanjie Su. 2022. “Semantic Segmentation of High-Resolution Remote Sensing Images Based on a Class Feature Attention Mechanism Fused with Deeplabv3+.” Computers & Geosciences 158 (January): 104969. https://doi.org/10.1016/j.cageo.2021.104969.
  • Wei, Pengliang, Ran Huang, Tao Lin, and Jingfeng Huang. 2022. “Rice Mapping in Training Sample Shortage Regions Using a Deep Semantic Segmentation Model Trained on Pseudo-Labels.” Remote Sensing 14 (2): 328. https://doi.org/10.3390/rs14020328.
  • Xu, Zhiyong, Weicun Zhang, Tianxiang Zhang, and Jiangyun Li. 2021. “HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images.” Remote Sensing 13 (1), https://doi.org/10.3390/rs13010071.
  • Yang, Zenan, Haipeng Niu, Liang Huang, Xiaoxuan Wang, and Liangxin Fan. 2022. “Automatic Segmentation Algorithm for High-Spatial-Resolution Remote Sensing Images Based on Self-Learning Super-Pixel Convolutional Network.” International Journal of Digital Earth 15 (1): 1101–1124. https://doi.org/10.1080/17538947.2022.2083247.
  • Yang, Fengting, Qian Sun, Hailin Jin, and Zihan Zhou. 2020. “Superpixel Segmentation With Fully Convolutional Networks.” The Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 13964–13973.
  • Yu, Fisher., and Vladlen Koltun. 2016. “Multi-Scale Context Aggregation by Dilated Convolutions.” Proceedings of the International Conference on Learning Representations(ICLR).
  • Yuan, X. H., J. F. Shi, and L. C. Gu. 2021. “A Review of Deep Learning Methods for Semantic Segmentation of Remote Sensing Imagery.” Expert Systems with Applications 169: 114417. https://doi.org/10.1016/j.eswa.2020.114417.
  • Zhao, Wei, Yi Fu, Xiaosong Wei, and Hai Wang. 2018. “An Improved Image Semantic Segmentation Method Based on Superpixels and Conditional Random Fields.” Applied Sciences 8 (5): 837. https://doi.org/10.3390/app8050837.
  • Zhao, Danpei, Bo Yuan, Yue Gao, Xinhu Qi, and Zhenwei Shi. 2022. “UGCNet: An Unsupervised Semantic Segmentation Network Embedded With Geometry Consistency for Remote-Sensing Images.” IEEE Geoscience and Remote Sensing Letters 19: 1–5. https://doi.org/10.1109/LGRS.2021.3129776.
  • Zhao, Yang, Yuan Yuan, Feiping Nie, and Qi Wang. 2018. “Spectral Clustering Based on Iterative Optimization for Large-Scale and High-Dimensional Data.” Neurocomputing 318: 227–235. https://doi.org/10.1016/j.neucom.2018.08.059.