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Review

Crop field extraction from high resolution remote sensing images based on semantic edges and spatial structure map

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Article: 2302176 | Received 30 Oct 2023, Accepted 15 Dec 2023, Published online: 24 Jan 2024

References

  • Badrinarayanan V, Kendall A, Cipolla R. 2017. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 39(12):2481–2495. doi:10.1109/TPAMI.2016.2644615.
  • Bastani F, He S, Abbar S, Alizadeh M, Balakrishnan H, Chawla S, Madden S, DeWitt D. 2018. Roadtracer: automatic extraction of road networks from aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Bertasius G, Shi J, Torresani L. 2015. Deepedge: a multi-scale bifurcated deep network for top-down contour detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Bins LS, Fonseca LG, Erthal GJ, Ii FM. 1996. Satellite imagery segmentation: a region growing approach. Simpósio Brasileiro de Sensoriamento Remoto. 8(1996):677–680.
  • Canny J. 1986. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell. PAMI-8(6):679–698. doi:10.1109/TPAMI.1986.4767851.
  • Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S. 2020. End-to-end object detection with transformers. Paper presented at the Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16.
  • Chen H, Wang Y, Guo T, Xu C, Deng Y, Liu Z, Ma S, Xu C, Xu C, Gao W. 2021. Pre-trained image processing transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • 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 Trans Pattern Anal Mach Intell. 40(4):834–848. doi:10.1109/TPAMI.2017.2699184.
  • Chen M, Radford A, Child R, Wu J, Jun H, Luan D, Sutskever I. 2020. Generative pretraining from pixels. In: Daumé Hal, III and Singh Aarti, editors. Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research. p. 1691–1703.
  • Chu H, Li D, Acuna D, Kar A, Shugrina M, Wei X, Liu M-Y, Torralba A, Fidler S. 2019. Neural turtle graphics for modeling city road layouts. In: Proceedings of the IEEE/CVF International Conference on Computer Vision.
  • Dai J, Qi H, Xiong Y, Li Y. 2017. GuodongZhang, Han Hu, and Yichen Wei. Deformable convolutionalnetworks. Paper Presented at the ICCV.
  • Diakogiannis FI, Waldner F, Caccetta P. 2021. Looking for change? Roll the dice and demand attention. Remote Sensing. 13(18):3707. doi:10.3390/rs13183707.
  • Diakogiannis FI, Waldner F, Caccetta P, Wu C. 2020. ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J Photogrammetry Remote Sens. 162:94–114. doi:10.1016/j.isprsjprs.2020.01.013.
  • Fan J, Yau DK, Elmagarmid AK, Aref WG. 2001. Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process. 10(10):1454–1466. doi:10.1109/83.951532.
  • Firdaus-Nawi M, Noraini O, Sabri MY, Siti-Zahrah A, Zamri-Saad M, Latifah H. 2011. DeepLabv3+ _encoder-decoder with Atrous separable convolution for semantic image segmentation. Pertanika J Trop Agric Sci. 34(1):137–143.
  • 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.
  • He J, Zhang S, Yang M, Shan Y, Huang T. 2019. Bi-directional cascade network for perceptual edge detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • He S, Bastani F, Jagwani S, Alizadeh M, Balakrishnan H, Chawla S, Elshrif MM, Madden S, Sadeghi MA. 2020. Sat2graph: road graph extraction through graph-tensor encoding. Paper presented at the Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIV 16.
  • He S, Bastani F, Jagwani S, Park E, Abbar S, Alizadeh M, Balakrishnan H, Chawla S, Madden S, Sadeghi MA. 2020. Roadtagger: robust road attribute inference with graph neural networks. Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence. doi:10.1609/aaai.v34i07.6730.
  • Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. 2017. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y-W, Wu J. 2020. Unet 3+: a full-scale connected unet for medical image segmentation. Paper presented at the ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
  • Kolesnikov A, Dosovitskiy A, Weissenborn D, Heigold G, Uszkoreit J, Beyer L, Minderer M, Dehghani M, Houlsby N, Gelly S. 2021. An Image is Worth 16x16 Words: transformers for Image Recognition at Scale.
  • 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.
  • Liu Y, Cheng M-M, Hu X, Wang K, Bai X. 2017. Richer convolutional features for edge detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Long J, Li M, Wang X, Stein A. 2022. Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images. Inter J Appl Earth Observ Geoinform. 112:102871. doi:10.1016/j.jag.2022.102871.
  • Maggiori E, Tarabalka Y, Charpiat G, Alliez P. 2017. Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans Geosci Remote Sensing. 55(2):645–657. doi:10.1109/TGRS.2016.2612821.
  • Peng C, Zhang X, Yu G, Luo G, Sun J. 2017. Large kernel matters–improve semantic segmentation by global convolutional network. Paper Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Poma XS, Riba E, Sappa A. 2020. Dense extreme inception network: towards a robust cnn model for edge detection. Paper Presented at the Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.
  • Prishchepov A, Radeloff V, Buchner J, Yin H, Kuemmerle T, Bleyhl B. 2018. Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series.
  • Pu M, Huang Y, Liu Y, Guan Q, Ling H. 2022. Edter: edge detection with transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Ronneberger O, Fischer P, Brox T. 2015. U-net: convolutional networks for biomedical image segmentation. Paper presented at the Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18.
  • Shit S, Koner R, Wittmann B, Paetzold J, Ezhov I, Li H, Pan J, Sharifzadeh S, Kaissis G, Tresp V. 2022. Relationformer: a unified framework for image-to-graph generation. Paper presented at the Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVII.
  • Stewart R, Andriluka M, Ng AY. 2016. End-to-end people detection in crowded scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Su Z, Liu W, Yu Z, Hu D, Liao Q, Tian Q, Pietikäinen M, Liu L. 2021. Pixel difference networks for efficient edge detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision.
  • Tan Y-Q, Gao S-H, Li X-Y, Cheng M-M, Ren B. 2020. Vecroad: point-based iterative graph exploration for road graphs extraction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Tremeau A, Borel N. 1997. A region growing and merging algorithm to color segmentation. Pattern Recognition. 30(7):1191–1203. doi:10.1016/S0031-3203(96)00147-1.
  • Volpi M, Tuia D. 2018. Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images. ISPRS J Photogrammetry Remote Sens. 144:48–60. doi:10.1016/j.isprsjprs.2018.06.007.
  • Wagner MP, Oppelt N. 2020. Deep learning and adaptive graph-based growing contours for agricultural field extraction. Remote Sensing. 12(12):1990. doi:10.3390/rs12121990.
  • Waldner F, Diakogiannis FI. 2020. Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network. Remote Sens Environ. 245:111741. doi:10.1016/j.rse.2020.111741.
  • Wang G, Wang X, Li FW, Liang X. 2019. Doobnet: deep object occlusion boundary detection from an image. Paper presented at the Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part VI 14.
  • Wang S, Waldner F, Lobell DB. 2022. Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision. Remote Sens. 14(22):5738. doi:10.3390/rs14225738.
  • Wei Y, Zhang K, Ji S. 2020. Simultaneous road surface and centerline extraction from large-scale remote sensing images using CNN-based segmentation and tracing. IEEE Trans Geosci Remote Sensing. 58(12):8919–8931. doi:10.1109/TGRS.2020.2991733.
  • Xia L, Luo J, Sun Y, Yang H. 2018. Deep extraction of cropland parcels from very high-resolution remotely sensed imagery. Paper presented at the 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics). doi:10.1109/Agro-Geoinformatics.2018.8476002.
  • Xia N, Wang Y, Xu H, Sun Y, Yuan Y, Cheng L, Jiang P, Li M. 2016. Demarcation of prime farmland protection areas around a metropolis based on high-resolution satellite imagery. Sci Rep. 6(1):37634. doi:10.1038/srep37634.
  • Xie S, Tu Z. 2015. Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision.
  • Xu L, Yang P, Yu J, Peng F, Xu J, Song S, Wu Y. 2023. Extraction of cropland field parcels with high resolution remote sensing using multi-task learning. Europ J Remote Sens. 56(1):2181874. doi:10.1080/22797254.2023.2181874.
  • Yang J, Price B, Cohen S, Lee H, Yang M-H. 2016. Object contour detection with a fully convolutional encoder-decoder network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Zhang H, Liu M, Wang Y, Shang J, Liu X, Li B, Song A, Li Q. 2021. Automated delineation of agricultural field boundaries from Sentinel-2 images using recurrent residual U-Net. Inter J Appl Earth Observ Geoinform. 105:102557. doi:10.1016/j.jag.2021.102557.
  • Zhang TY, Suen CY. 1984. A fast parallel algorithm for thinning digital patterns. Commun ACM. 27(3):236–239. doi:10.1145/357994.358023.
  • Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, Fu Y, Feng J, Xiang T, Torr PH. 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.
  • Zhou L, Zhang C, Wu M. 2018. D-LinkNet: linkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.
  • Zhu XX, 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 Geosci Remote Sens Mag. 5(4):8–36. doi:10.1109/MGRS.2017.2762307.
  • Zhu X, Su W, Lu L, Li B, Wang X, Dai J. 2020. Deformable DETR: deformable Transformers for End-to-End Object Detection. Paper presented at the International Conference on Learning Representations.