Abstract
In recent years, semantic segmentation has become an important means of processing remote sensing images, and it is widely used in various fields such as natural disaster detection, environmental protection, and land resource management. In response to this, the mainstream method of the deep convolutional network is constantly innovating and iterating. However, previous methods usually do not fully exploit the information associations between different representations, and the information of low-level representations is usually not well applied. In response to this, we propose a multi-representation decoupled joint network (MDJN) based on a three-branch architecture to improve the performance of semantic segmentation on remote sensing images, which utilizes multi-representation decoupling (MRD) to decouple the original single-branch network into the main branch, body branch and edge branch to enhance information fusion for different representations. Specifically, based on representation learning, we first propose a cross-representation graph convolution (CGC) module to fully mine and learn the context information between different representations with the help of graph convolutional networks (GCN). Secondly, we propose a new three-branch information interaction (TII) module to perform three-way interaction for the information of the three branches, so that the intra-class consistency and inter-class expressivity between different representations can fully play a role. The mean intersection over union (mIoU) of MDJN reaches 78.19% and 81.26% respectively on on both International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam datasets.
Similar content being viewed by others
Data Availability
All data analysed during this study are included in this published article [32].
References
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(12):2481–2495
Bhatti UA, Huang M, Wu D, Zhang Y, Mehmood A, Han H (2019) Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterp Inf Syst 13(3):329–351
Bhatti UA, Yu Z, Yuan L, Zeeshan Z, Nawaz SA, Bhatti M, Mehmood A, Ain QU, Wen L (2020) Geometric algebra applications in geospatial artificial intelligence and remote sensing image processing. IEEE Access 8:155783–155796
Bhatti UA, Yu Z, Chanussot J, Zeeshan Z, Yuan L, Luo W, Nawaz SA, Bhatti MA, Ain QU, Mehmood A (2021) Local similarity-based spatial-spectral fusion hyperspectral image classification with deep cnn and gabor filtering. IEEE Transactions on Geoscience and Remote Sensing 60:1–15
Bhatti UA, Ming-Quan Z, Qing-Song H, Ali S, Hussain A, Yuhuan Y, Yu Z, Yuan L, Nawaz SA (2021) Advanced color edge detection using clifford algebra in satellite images. IEEE Photon J 13(2):1–20
Bhatti UA, Zeeshan Z, Nizamani MM, Bazai S, Yu Z, Yuan L (2022) Assessing the change of ambient air quality patterns in jiangsu province of china pre-to post-covid-19. Chemosphere 288:132569
Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203
Chaurasia A, Culurciello E (2017) Linknet: Exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP). IEEE, pp 1–4
Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587
Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV). pp 801–818
Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40(4):834–848
Chen Y, Rohrbach M, Yan Z, Shuicheng Y, Feng J, Kalantidis Y (2019) Graph-based global reasoning networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp 433–442
Chen A, Zhou Y (2020) An attention enhanced graph convolutional network for semantic segmentation. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV). Springer, pp 734–745
Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 1251–1258
Chong Y, Chen X, Pan S (2020) Context union edge network for semantic segmentation of small-scale objects in very high resolution remote sensing images. IEEE Geoscience and Remote Sensing Letters
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, 29
Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp 3146–3154
Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International Conference on Machine Learning. PMLR, pp 1263–1272
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 770–778
Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp 603–612
Hu X, Yang K, Fei L, Wang K (2019) Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation. In: 2019 IEEE International Conference on Image Processing (ICIP). IEEE, pp 1440–1444
Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907
Li R, Zheng S, Zhang C, Duan C, Wang L, Atkinson PM (2021) Abcnet: Attentive bilateral contextual network for efficient semantic segmentation of fine-resolution remotely sensed imagery. ISPRS J Photogramm Remote Sens 181:84–98
Liang X, Hu Z, Zhang H, Lin L, Xing EP (2018) Symbolic graph reasoning meets convolutions. Advances in Neural Information Processing Systems, 31
Li X, Li X, Zhang L, Cheng G, Shi J, Lin Z, Tan S, Tong Y (2020) Improving semantic segmentation via decoupled body and edge supervision. In: European Conference on Computer Vision. Springer, pp 435–452
Lin D, Zhang R, Ji Y, Li P, Huang H (2018) Scn: Switchable context network for semantic segmentation of rgb-d images. IEEE Trans Cybern 50(3):1120–1131
Lin G, Milan A, Shen C, Reid I (2017) Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 1925–1934
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 3431–3440
Niu R, Sun X, Tian Y, Diao W, Chen K, Fu K (2021) Hybrid multiple attention network for semantic segmentation in aerial images. IEEE Transactions on Geoscience and Remote Sensing 60:1–18
Nogueira K, Dalla Mura M, Chanussot J, Schwartz WR, Dos Santos JA (2019) Dynamic multicontext segmentation of remote sensing images based on convolutional networks. IEEE Trans Geosci Remote Sens 57(10):7503–7520
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention. Springer, pp 234–241
Rottensteiner F, Sohn G, Gerke M, Wegner JD (2014) Isprs semantic labeling contest. ISPRS: Leopoldshöhe, Germany 1:4
Sulla-Menashe D, Gray JM, Abercrombie SP, Friedl MA (2019) Hierarchical mapping of annual global land cover 2001 to present: The modis collection 6 land cover product. Remote Sens Environ 222:183–194
Takikawa T, Acuna D, Jampani V, Fidler S (2019) Gated-scnn: Gated shape cnns for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp 5229–5238
Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 7794–7803
Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems 34:12077–12090
Yin M, Yao Z, Cao Y, Li X, Zhang Z, Lin S, Hu H (2020) Disentangled non-local neural networks. In: European Conference on Computer Vision. pp 191–207
Yuan Y, Huang L, Guo J, Zhang C, Chen X, Wang J (2021) Ocnet: Object context for semantic segmentation. Int J Comput Vision 129(8):2375–2398
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European Conference on Computer Vision. Springer, pp 818–833
Zhang J, Feng L, Yao F (2014) Improved maize cultivated area estimation over a large scale combining modis-evi time series data and crop phenological information. ISPRS J Photogramm Remote Sens 94:102–113
Zhang C, Sargent I, Pan X, Li H, Gardiner A, Hare J, Atkinson PM (2019) Joint deep learning for land cover and land use classification. Remote Sensing of Environment 221:173–187
Zhang X, Wei Y, Yang Y, Huang TS (2020) Sg-one: Similarity guidance network for one-shot semantic segmentation. IEEE Trans Cybern 50(9):3855–3865
Zhang C, Harrison PA, Pan X, Li H, Sargent I, Atkinson PM (2020) Scale sequence joint deep learning (ss-jdl) for land use and land cover classification. Remote Sens Environ 237:111593
Zhang F, Chen Y, Li Z, Hong Z, Liu J, Ma F, Han J, Ding E (2019) Acfnet: Attentional class feature network for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp 6798–6807
Zhang J, Yang K, Constantinescu A, Peng K, Müller K, Stiefelhagen R (2021) Trans4trans: Efficient transformer for transparent object segmentation to help visually impaired people navigate in the real world. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp 1760–1770
Zhao H, Qi X, Shen X, Shi J, Jia J (2018) Icnet for real-time semantic segmentation on high-resolution images. In: Proceedings of the European Conference on Computer Vision (ECCV). pp 405–420
Zhao H, Shi J, Qi X, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 2881–2890
Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr PH et al (2021) Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp 6881–6890
Zhen M, Wang J, Zhou L, Li S, Shen T, Shang J, Fang T, Quan L (2020) Joint semantic segmentation and boundary detection using iterative pyramid contexts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp 13666–13675
Zhong Z, Lin ZQ, Bidart R, Hu X, Daya IB, Li Z, Zheng W-S, Li J, Wong A (2020) Squeeze-and-attention networks for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp 13065–13074
Zhu Z, Xu M, Bai S, Huang T, Bai X (2019) Asymmetric non-local neural networks for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp 593–602
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China(62072418, 62172376); the Major Scientific and Technological Innovation Project of Shandong (2019JZZY020705); the Fundamental Research Funds for the Central Universities (202042008).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lv, X., Wang, R., Zheng, C. et al. Multi-representation decoupled joint network for semantic segmentation of remote sensing images. Multimed Tools Appl 83, 13291–13313 (2024). https://doi.org/10.1007/s11042-023-15660-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-15660-y