Skip to main content

Advertisement

SpringerLink
Log in

Intelligent Image Semantic Segmentation: A Review Through Deep Learning Techniques for Remote Sensing Image Analysis

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Image semantic segmentation is an important part of fundamental in image interpretation and computer vision. With the development of convolutional neural network technology, deep learning-based image semantic segmentation methods have received more and more attention and research. At present, many excellent semantic segmentation methods have been proposed and applied in the field of remote sensing. In this paper, we summarized the semantic segmentation methods used for remote sensing image, including the traditional remote sensing image semantic segmentation methods and the methods based on deep learning, we emphasize on summarizing the remote sensing image semantic segmentation algorithms based on deep learning and classify them into different categories, and then we introduce the datasets that commonly used and data preparation methods including pre-processing and augmentation techniques. Finally, the challenges and future directions of research in this domain are analyzed and prospected. It is hoped that this study can widen the frontiers of knowledge and provide useful literature for researchers interested in advancing this field of research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Availability of Data and Material

Not applicable.

Code Availability

Not applicable.

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. https://doi.org/10.1109/TPAMI.2016.2644615

    Article  Google Scholar 

  • Bona, D. S., Murni, A., & Mursanto, P. (2019). Semantic segmentation and segmentation refinement using machine learning case study: Water turbidity segmentation. In Proceedings of the 2019 IEEE international conference on aerospace electronics and remote sensing technology, ICARES 2019. https://doi.org/10.1109/ICARES.2019.8914551.

  • Chen, G., Zhang, X., Wang, Q., Dai, F., Gong, Y., & Zhu, K. (2018a). Symmetrical dense-shortcut deep fully convolutional networks for semantic segmentation of very-high-resolution remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5), 1633–1644. https://doi.org/10.1109/JSTARS.2018.2810320

    Article  Google Scholar 

  • Chen, J., Chen, G., Wang, L., Fang, B., Zhou, P., & Zhu, M. (2020). Coastal land cover classification of high-resolution remote sensing images using attention-driven context encoding network. Sensors (switzerland), 20(24), 1–22. https://doi.org/10.3390/s20247032

    Article  Google Scholar 

  • Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014). Semantic image segmentation with deep convolutional nets and fully connected CRFs. Complex Variables, Theory and Application: An International Journal, 7(4), 357–361. https://doi.org/10.1080/17476938708814211

    Article  Google Scholar 

  • Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018b). 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. https://doi.org/10.1109/TPAMI.2017.2699184

    Article  Google Scholar 

  • Chen, L. C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv. Available at: http://arxiv.org/abs/1706.05587.

  • Chen, L. C., Yang, Y., Wang, J., Xu, W., & Yuille, A. L. (2016). Attention to scale: Scale-aware semantic image segmentation. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition, 2016-Decem (pp. 3640–3649). https://doi.org/10.1109/CVPR.2016.396.

  • Cheng, B., Liang, C., Liu, X., Liu, Y., Ma, X., & Wang, G. (2020). Research on a novel extraction method using deep learning based on GF-2 images for aquaculture areas. International Journal of Remote Sensing, 41(9), 3575–3591. https://doi.org/10.1080/01431161.2019.1706009

    Article  Google Scholar 

  • Cheng, G., Han, J., Zhou, P., & Guo, L. (2014). Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98, 119–132. https://doi.org/10.1016/j.isprsjprs.2014.10.002

    Article  Google Scholar 

  • Cui, B., Jing, W., Huang, L., Li, Z., & Lu, Y. (2021). SANet: A sea-land segmentation network via adaptive multiscale feature learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 116–126. https://doi.org/10.1109/JSTARS.2020.3040176

    Article  Google Scholar 

  • Cui, W., Wang, F., He, X., Zhang, D., Xu, X., Yao, M., Wang, Z., & Huang, J. (2019). Multi-scale semantic segmentation and spatial relationship recognition of remote sensing images based on an attention model. Remote Sensing, 11(9), 1044. https://doi.org/10.3390/rs11091044

    Article  Google Scholar 

  • Dong, R., Li, W., Fu, H., Gan, L., Yu, L., Zheng, J., & Xia, M. (2020). Oil palm plantation mapping from high-resolution remote sensing images using deep learning. International Journal of Remote Sensing, 41(5), 2022–2046. https://doi.org/10.1080/01431161.2019.1681604

    Article  Google Scholar 

  • Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2), 303–338. https://doi.org/10.1007/s11263-009-0275-4

    Article  Google Scholar 

  • Fu, G., Liu, C., Zhou, R., Sun, T., & Zhang, Q. (2017). Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sensing, 9(5), 1–21. https://doi.org/10.3390/rs9050498

    Article  Google Scholar 

  • Gao, J., Wang, H., & Shen, H. (2020). Task failure prediction in cloud data centers using deep learning. IEEE Transactions on Services Computing. https://doi.org/10.1109/tsc.2020.2993728

    Article  Google Scholar 

  • Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S. R., Tiede, D., & Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing, 11(2), 196. https://doi.org/10.3390/rs11020196

    Article  Google Scholar 

  • Girshick, R. (2015). Fast R-CNN. In Proceedings of the IEEE international conference on computer vision, 2015 Inter (pp. 1440–1448). https://doi.org/10.1109/ICCV.2015.169.

  • Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). ‘Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE computer society conference on computer vision and pattern recognition (pp. 580–587). https://doi.org/10.1109/CVPR.2014.81.

  • Guo, H., He, G., Jiang, W., Yin, R., Yan, L., & Leng, W. (2020). A multi-scale water extraction convolutional neural network (MWEN) method for GaoFen-1 remote sensing images. ISPRS International Journal of Geo-Information, 9(4), 1–20. https://doi.org/10.3390/ijgi90401899

    Article  Google Scholar 

  • Guo, S., Jin, Q., Wang, H., Wang, X., Wang, Y., & Xiang, S. (2019). Learnable gated convolutional neural network for semantic segmentation in remote-sensing images. Remote Sensing, 11(16), 1–22. https://doi.org/10.3390/rs11161922

    Article  Google Scholar 

  • Han, Z., Dian, Y., Xia, H., Zhou, J., Jian, Y., Yao, C., Wang, X., & Li, Y. (2020). Comparing fully deep convolutional neural networks for land cover classification with high-spatial-resolution gaofen-2 images. ISPRS International Journal of Geo-Information, 9(8), 478. https://doi.org/10.3390/ijgi9080478

    Article  Google Scholar 

  • He, C., Fang, P., Zhang, Z., Xiong, D., & Liao, M. (2019a). An end-to-end conditional random fields and skip-connected generative adversarial segmentation network for remote sensing images. Remote Sensing, 11(13), 1–22. https://doi.org/10.3390/rs11131604

    Article  Google Scholar 

  • He, C., Li, S., Xiong, D., Fang, P., & Liao, M. (2020). Remote sensing image semantic segmentation based on edge information guidance. Remote Sensing, 12(9), 1–20. https://doi.org/10.3390/RS12091501

    Article  Google Scholar 

  • He, H., Yang, D., Wang, S., Wang, S., & Li, Y. (2019b). Road extraction by using atrous spatial pyramid pooling integrated encoder–decoder network and structural similarity loss. Remote Sensing, 11(9), 1–16. https://doi.org/10.3390/rs11091015

    Article  Google Scholar 

  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 770–778). IEEE. https://doi.org/10.1109/CVPR.2016.90.

  • Hu, H., Li, Z., Li, L., Yang, H., & Zhu, H. (2020). Classification of very high-resolution remote sensing imagery using a fully convolutional network with global and local context information enhancements. IEEE Access, 8, 14606–14619. https://doi.org/10.1109/ACCESS.2020.2964760

    Article  Google Scholar 

  • Huang, Q., Xia, C., Wu, C., Li, S., Wang, Y., Song, Y., & Kuo, C. C. J. (2017). Semantic segmentation with reverse attention. In British machine vision conference 2017.

  • Hung, W. C., Tsai, Y. H., Liou, Y. T., Lin, Y. Y., & Yang, M. H. (2018). Adversarial learning for semi-supervised semantic segmentation. https://arxiv.org/1802.07934v2.

  • Ji, S., Wei, S., & Lu, M. (2019). Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set. IEEE Transactions on Geoscience and Remote Sensing, 57(1), 574–586. https://doi.org/10.1109/TGRS.2018.2858817

    Article  Google Scholar 

  • Kattenborn, T., Eichel, J., & Fassnacht, F. E. (2019). Convolutional neural networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Scientific Reports, 9(1), 1–9. https://doi.org/10.1038/s41598-019-53797-9

    Article  Google Scholar 

  • Kniaz, V. V. (2018). Conditional GANs for semantic segmentation of multispectral satellite images. In L. Bruzzone, F. Bovolo, & J. A. Benediktsson (Eds.), Image and signal processing for remote sensing XXIV (p. 28). SPIE. https://doi.org/10.1117/12.2325601

    Chapter  Google Scholar 

  • Kniaz, V. V. (2019). Deep learning for dense labeling of hydrographic regions in very high resolution imagery. In L. Bruzzone, F. Bovolo, & J. A. Benediktsson (Eds.), Image and signal processing for remote sensing XXV (p. 63). SPIE. https://doi.org/10.1117/12.2533161

    Chapter  Google Scholar 

  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  • Krogh Mortensen, A., Dyrmann, M., Karstoft, H., Jørgensen, R. N., & Gislum, R. (2016). Semantic segmentation of mixed crops using deep convolutional neural network. In CIGR-AgEng conference (pp. 1–6). Available at: www.elementar.de.

  • Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778–782. https://doi.org/10.1109/LGRS.2017.2681128

    Article  Google Scholar 

  • Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 34, 436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  • Li, H., Xiong, P., An, J., & Wang, L. (2018a). Pyramid attention network for semantic segmentation. https://arxiv.org/1805.10180v3.

  • Li, R., Liu, W., Yang, L., Sun, S., Hu, W., Zhang, F., & Li, W. (2018b). DeepUNet: A deep fully convolutional network for pixel-level sea-land segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(11), 3954–3962. https://doi.org/10.1109/JSTARS.2018.2833382

    Article  Google Scholar 

  • Li, Q., Shi, Y., Huang, X., & Zhu, X. X. (2020). Building footprint generation by integrating convolution neural network with feature pairwise conditional random field (FPCRF). IEEE Transactions on Geoscience and Remote Sensing, 58(11), 7502–7519. https://doi.org/10.1109/TGRS.2020.2973720

    Article  Google Scholar 

  • Li, Z., Wang, R., Zhang, W., Hu, F., & Meng, L. (2019). Multiscale features supported DeepLabV3+ optimization scheme for accurate water semantic segmentation. IEEE Access, 7, 155787–155804. https://doi.org/10.1109/ACCESS.2019.2949635

    Article  Google Scholar 

  • Lin, G., Milan, A., Shen, C., & Reid, I. (2017a). 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). https://doi.org/10.1109/CVPR.2017.549.

  • Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017b) Feature pyramid networks for object detection. In IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society.

  • Liu, S., Ding, W., Li, H., & Liu, C. (2019) PLFCN: Pyramid loss reinforced fully convolutional network. In ACM international conference proceeding series. https://doi.org/10.1145/3349801.3349819.

  • Liu, W., Zhang, Y., Fan, H., Zou, Y., & Cui, Z. (2020). A new multi-channel deep convolutional neural network for semantic segmentation of remote sensing image. IEEE Access, 8, 131814–131825. https://doi.org/10.1109/ACCESS.2020.3009976

    Article  Google Scholar 

  • Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In 2015 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3431–3440). IEEE. https://doi.org/10.1109/CVPR.2015.7298965.

  • Lopez, J., Santos, S., Atzberger, C., & Torres, D. (2019). Convolutional neural networks for semantic segmentation of multispectral remote sensing images. In Proceedings—2018 10th IEEE Latin–American conference on communications, LATINCOM 2018 (pp. 1–5). https://doi.org/10.1109/LATINCOM.2018.8613216.

  • Luc, P., Couprie, C., Chintala, S., & Verbeek, J. (2016). Semantic segmentation using adversarial networks. Available at: http://arxiv.org/abs/1611.08408.

  • Mangalraj, P., Sivakumar, V., Karthick, S., Haribaabu, V., Ramraj, S., & Samuel, D. J. (2019). A review of multi-resolution analysis (MRA) and multi-geometric analysis (MGA) tools used in the fusion of remote sensing images. Circuits, Systems, and Signal Processing, 39(6), 3145–3172. https://doi.org/10.1007/s00034-019-01316-6

    Article  Google Scholar 

  • Mnih, V. (2013). Machine learning for aerial image labeling. PhD Thesis.

  • Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7(September), 1–10. https://doi.org/10.3389/fpls.2016.01419

    Article  Google Scholar 

  • Ni, X., Cheng, Y., & Wang, Z. (2019). Remote sensing semantic segmentation with convolution neural network using attention mechanism. In 2019 14th IEEE international conference on electronic measurement and instruments, ICEMI 2019 (pp. 608–613).

  • Osco, L. P., Nogueira, K., Ramos, A. P., Pinheiro, M. M., Furuya, D. E., Gonçalves, W. N., de Castro Jorge, L. A., Junior, J. M., & dos Santos, J. A. (2021). Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery. Precision Agriculture. https://doi.org/10.1007/s11119-020-09777-5

    Article  Google Scholar 

  • Pan, X., Zhao, J., & Xu, J. (2020a). An end-to-end and localized post-processing method for correcting high-resolution remote sensing classification result images. Remote Sensing, 12(5), 852. https://doi.org/10.3390/rs12050852

    Article  Google Scholar 

  • Pan, Z., Dou, H., Mao, J., Dai, M., & Tian, J. (2018). MIFNet: Multi-information fusion network for sea-land segmentation. In ACM international conference proceeding series (pp. 24–29). ACM Press. https://doi.org/10.1145/3239576.3239578.

  • Pan, Z., Xu, J., Guo, Y., Hu, Y., & Wang, G. (2020b). Deep learning segmentation and classification for urban village using a worldview satellite image based on U-net. Remote Sensing, 12(10), 1574. https://doi.org/10.3390/rs12101574

    Article  Google Scholar 

  • Panboonyuen, T., Jitkajornwanich, K., Lawawirojwong, S., Srestasathiern, P., & Vateekul, P. (2019). Semantic segmentation on remotely sensed images using an enhanced global convolutional network with channel attention and domain specific transfer learning. Remote Sensing, 11(1), 1–22. https://doi.org/10.3390/rs11010083

    Article  Google Scholar 

  • Peng, C., Li, Y., Jiao, L., Chen, Y., & Shang, R. (2019). Densely based multi-scale and multi-modal fully convolutional networks for high-resolution remote-sensing image semantic segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(8), 2612–2626. https://doi.org/10.1109/JSTARS.2019.2906387

    Article  Google Scholar 

  • Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In IEEE Access (pp. 234–241). https://doi.org/10.1007/978-3-319-24574-4_28.

  • Shang, R., Zhang, J., Jiao, L., Li, Y., Marturi, N., & Stolkin, R. (2020). Multi-scale adaptive feature fusion network for semantic segmentation in remote sensing images. Remote Sensing, 12(5), 1–20. https://doi.org/10.3390/rs12050872

    Article  Google Scholar 

  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition (pp. 1–13). Available at: https://arxiv.org/abs/1409.1556.

  • Soloy, A., Turki, I., Fournier, M., Costa, S., Peuziat, B., & Lecoq, N. (2020). A deep learning-based method for quantifying and mapping the grain size on pebble beaches. Remote Sensing, 12(21), 3659.

    Article  Google Scholar 

  • Song, A., & Kim, Y. (2020). Semantic segmentation of remote-sensing imagery using heterogeneous big data: International society for photogrammetry and remote sensing potsdam and cityscape datasets. ISPRS International Journal of Geo-Information, 9(10), 601. https://doi.org/10.3390/ijgi9100601

    Article  Google Scholar 

  • Song, S., Liu, J., Liu, Y., Feng, G., Han, H., Yao, Y., & Du, M. (2020). Intelligent object recognition of urban water bodies based on deep learning for multi-source and multi-temporal high spatial resolution remote sensing imagery. Sensors (switzerland), 20(2), 397. https://doi.org/10.3390/s20020397

    Article  Google Scholar 

  • Sumbul, G., Charfuelan, M., Demir, B., & Markl, V. (2019). Bigearthnet: A large-scale benchmark archive for remote sensing image understanding. In arXiv (pp. 2–5). https://doi.org/10.1109/igarss.2019.8900532.

  • Sun, X., Shi, A., Huang, H., & Mayer, H. (2020). BAS Net: Boundary-aware semi-supervised semantic segmentation network for very high resolution remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5398–5413. https://doi.org/10.1109/JSTARS.2020.3021098

    Article  Google Scholar 

  • Tong, X. Y., Xia, G. S., Lu, Q., Shen, H., Li, S., You, S., & Zhang, L. (2020). Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237, 111322. https://doi.org/10.1016/j.rse.2019.111322

    Article  Google Scholar 

  • Venugopal, N. (2020). Automatic semantic segmentation with DeepLab dilated learning network for change detection in remote sensing images. Neural Processing Letters, 51(3), 2355–2377. https://doi.org/10.1007/s11063-019-10174-x

    Article  Google Scholar 

  • Wang, S., Mu, X., Yang, D., He, H., & Zhao, P. (2020a). Attention guided encoder–decoder network with multi-scale context aggregation for land cover segmentation. IEEE Access, 8, 215299–215309. https://doi.org/10.1109/ACCESS.2020.3040862

    Article  Google Scholar 

  • Wang, W., Samuel, R. D., & Hsu, C. (2020b). Prediction architecture of deep learning assisted short long term neural network for advanced traffic critical prediction system using remote sensing data. European Journal of Remote Sensing, 54(Sup2), 65–76. https://doi.org/10.1080/22797254.2020.1755998

    Article  Google Scholar 

  • Wang, Z., Gao, X., Zhang, Y., & Zhao, G. (2020c). Mslwenet: A novel deep learning network for lake water body extraction of google remote sensing images. Remote Sensing, 12(24), 1–19. https://doi.org/10.3390/rs12244140

    Article  Google Scholar 

  • Weng, L., Xu, Y., Xia, M., Zhang, Y., Liu, J., & Xu, Y. (2020). Water areas segmentation from remote sensing images using a separable residual SegNet network. ISPRS International Journal of Geo-Information, 9(4), 256. https://doi.org/10.3390/ijgi9040256

    Article  Google Scholar 

  • Wu, J., Yang, G., Yang, H., Zhu, Y., Li, Z., Lei, L., & Zhao, C. (2020a). Extracting apple tree crown information from remote imagery using deep learning. Computers and Electronics in Agriculture, 174, 105504. https://doi.org/10.1016/j.compag.2020.105504

    Article  Google Scholar 

  • Wu, W., Gao, X., Fan, J., Xia, L., Luo, J., & Zhou, Y. N. (2020b). Improved mask R-CNN-based cloud masking method for remote sensing images. International Journal of Remote Sensing, 41(23), 8908–8931. https://doi.org/10.1080/01431161.2020.1792576

    Article  Google Scholar 

  • Xia, M., Cui, Y., Zhang, Y., Xu, Y., Liu, J., & Xu, Y. (2021a). DAU-Net: A novel water areas segmentation structure for remote sensing image. International Journal of Remote Sensing, 42(7), 2594–2621. https://doi.org/10.1080/01431161.2020.1856964

    Article  Google Scholar 

  • Xia, M., Wang, T., Zhang, Y., Liu, J., & Xu, Y. (2021b). Cloud/shadow segmentation based on global attention feature fusion residual network for remote sensing imagery. International Journal of Remote Sensing, 42(6), 2022–2045. https://doi.org/10.1080/01431161.2020.1849852

    Article  Google Scholar 

  • Xiong, D., He, C., Liu, X., & Liao, M. (2020). An end-to-end Bayesian segmentation network based on a generative adversarial network for remote sensing images. Remote Sensing, 12(2), 216. https://doi.org/10.3390/rs12020216

    Article  Google Scholar 

  • Xu, Q., Yuan, X., Ouyang, C., & Zeng, Y. (2020). Attention-based pyramid network for segmentation and classification of high-resolution and hyperspectral remote sensing images. Remote Sensing, 12(21), 1–34. https://doi.org/10.3390/rs12213501

    Article  Google Scholar 

  • Yang, Q., Liu, M., Zhang, Z., Yang, S., Ning, J., & Han, W. (2019). Mapping plastic mulched farmland for high resolution images of unmanned aerial vehicle using deep semantic segmentation. Remote Sensing. https://doi.org/10.3390/rs11172008

    Article  Google Scholar 

  • Yu, L., Ma, F., Jayasuriya, A., Sigelle, M., & Perreau, S. (2007). A new contour detection approach in mammogram using rational wavelet filtering and MRF smoothing. In Proceedings—Digital image computing techniques and applications: 9th biennial conference of the Australian pattern recognition society, DICTA 2007 (pp. 106–111). https://doi.org/10.1109/DICTA.2007.4426783.

  • Yu, T., Wu, W., Gong, C., & Li, X. (2021). Residual multi-attention classification network for a forest dominated tropical landscape using high-resolution remote sensing imagery. ISPRS International Journal of Geo-Information, 10(1), 22. https://doi.org/10.3390/ijgi10010022

    Article  Google Scholar 

  • Zhan, Z., Zhang, X., Liu, Y., Sun, X., Pang, C., & Zhao, C. (2020). Vegetation land use/land cover extraction from high-resolution satellite images based on adaptive context inference. IEEE Access, 8, 21036–21051. https://doi.org/10.1109/ACCESS.2020.2969812

    Article  Google Scholar 

  • Zhang, M., & Hu, X. (2017). Translation-aware semantic segmentation via conditional least-square generative adversarial networks. Journal of Applied Remote Sensing, 11(04), 042622. https://doi.org/10.1117/1.jrs.11.042622

    Article  Google Scholar 

  • Zhang, W., Witharana, C., Liljedahl, A. K., & Kanevskiy, M. (2018). Deep convolutional neural networks for automated characterization of arctic ice-wedge polygons in very high spatial resolution aerial imagery. Remote Sensing, 10(9), 1487. https://doi.org/10.3390/rs10091487

    Article  Google Scholar 

  • Zhang, X., Xiao, Z., Li, D., Fan, M., & Zhao, L. (2019). Semantic segmentation of remote sensing images using multiscale decoding network. IEEE Geoscience and Remote Sensing Letters, 16(9), 1492–1496. https://doi.org/10.1109/lgrs.2019.2901592

    Article  Google Scholar 

  • Zhang, Y., & Chi, M. (2020). Mask-R-FCN: A deep fusion network for semantic segmentation. IEEE Access, 8, 155753–155765. https://doi.org/10.1109/ACCESS.2020.3012701

    Article  Google Scholar 

  • Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) Pyramid scene parsing network. In Proceedings—30th IEEE conference on computer vision and pattern recognition, CVPR 2017 (pp. 6230–6239). https://doi.org/10.1109/CVPR.2017.660.

  • Zhao, L., Tang, P., & Huo, L. (2016). Feature significance-based multibag-of-visual-words model for remote sensing image scene classification. Journal of Applied Remote Sensing, 10(3), 035002. https://doi.org/10.1117/1.jrs.10.035004

    Article  Google Scholar 

  • Zhao, T., Yang, Y., Niu, H., Wang, D., & Chen, Y. (2018). Comparing U-Net convolutional networks with fully convolutional networks in the performances of pomegranate tree canopy segmentation. In A. M. Larar, M. Suzuki, & J. Wang (Eds.), Multispectral, hyperspectral, and ultraspectral remote sensing technology, techniques and applications VII (p. 64). SPIE. https://doi.org/10.1117/12.2325570

    Chapter  Google Scholar 

  • Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018). PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 197–209. https://doi.org/10.1016/j.isprsjprs.2018.01.004

    Article  Google Scholar 

  • Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., & Jiao, J. (2015). Orientation robust object detection in aerial images using deep convolutional neural network. In Proceedings—International conference on image processing, ICIP (pp. 3735–3739). https://doi.org/10.1109/ICIP.2015.7351502.

  • Zhu, Q., Li, Z., Zhang, Y., & Guan, Q. (2020). Building extraction from high spatial resolution remote sensing images via multiscale-aware and segmentation-prior conditional random fields. Remote Sensing, 12(23), 1–18. https://doi.org/10.3390/rs12233983

    Article  Google Scholar 

  • Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8–36. https://doi.org/10.1109/MGRS.2017.2762307

    Article  Google Scholar 

  • Zou, J., Li, W., & Du, Q. (2015). deep learning based feature selection for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 12(11), 2321–2325. https://doi.org/10.1109/LGRS.2015.2475299

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by China Scholarship Council, State Key Laboratory of Geo-Information Engineering (No. SKLGIE2019-Z-4-1), Key Laboratory of Geological Survey and Evaluation of Ministry of Education (No. GLAB2020ZR11), the Fundamental Research Funds for the Central Universities, and National Natural Science Foundation of China (41871305).

Funding

No funding support was received.

Author information

Authors and Affiliations

  1. State Key Laboratory of Geo-Information Engineering, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, Hubei, China

    Baode Jiang, Shaofen Xu & Zhanlong Chen

  2. Xi’an Research Institute of Surveying and Mapping, Xi’an, 710054, Shaanxi, China

    Xiaoya An

Authors
  1. Baode Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoya An
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaofen Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhanlong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

BJ contributed to methodology, project administration, and manuscript editing; XA contributed to software and validation; SX performed visualization, and manuscript review and editing; ZC helped with design framework, resources, and validation.

Corresponding author

Correspondence to Xiaoya An.

Ethics declarations

Conflict of interest

The authors report no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, B., An, X., Xu, S. et al. Intelligent Image Semantic Segmentation: A Review Through Deep Learning Techniques for Remote Sensing Image Analysis. J Indian Soc Remote Sens 51, 1865–1878 (2023). https://doi.org/10.1007/s12524-022-01496-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-022-01496-w

Keywords

Search

Navigation