{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:35:30Z","timestamp":1764174930639,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19080301"],"award-info":[{"award-number":["XDA19080301"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701399"],"award-info":[{"award-number":["41701399"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Irrigation is indispensable in agriculture. Center pivot irrigation systems are popular means of irrigation since they are water-efficient and labor-saving. Monitoring center pivot irrigation systems provides important information for the understanding of agricultural production, water resources consumption and environmental change. Deep learning has become an effective approach for object detection and semantic segmentation. Recent studies have shown that convolutional neural networks (CNNs) are prone to be texture-biased rather than shape-biased, and increasing shape bias can improve the robustness and performance of CNNs. In this study, a simple yet effective method was proposed to increase shape bias in object detection networks to improve the precision of center pivot irrigation system detection. We extracted edge images of training samples and integrated them into the training data to increase shape bias in the networks. With the proposed shape increasing training scheme, we evaluated and compared PVANET and YOLOv4. Experiments with the images in Mato Grosso have shown that both PVANET and YOLOv4 achieved improved performance, which demonstrated the validity of the proposed method.<\/jats:p>","DOI":"10.3390\/rs13040612","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T04:33:46Z","timestamp":1612931626000},"page":"612","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Increasing Shape Bias to Improve the Precision of Center Pivot Irrigation System Detection"],"prefix":"10.3390","volume":"13","author":[{"given":"Jiwen","family":"Tang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4549-3502","authenticated-orcid":false,"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7140-8105","authenticated-orcid":false,"given":"Lijun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Ping","family":"Tang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.apgeog.2011.08.007","article-title":"Analyzing the agricultural transition in Mato Grosso, Brazil, using satellite-derived indices","volume":"32","author":"Arvor","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_2","first-page":"587","article-title":"The Nebraska Center-Pivot Inventory: An Example of Operational Satellite Remote Sensing on a Long-Term Basis","volume":"55","author":"Rundquist","year":"1989","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/361237.361242","article-title":"Use of the Hough transformation to detect lines and curves in pictures","volume":"15","author":"Duda","year":"1972","journal-title":"Commun. ACM"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_5","unstructured":"Tan, M., and Le, Q.V. (2020). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_8","unstructured":"Shelhamer, E., Long, J., and Darrell, T. (2016). Fully Convolutional Networks for Semantic Segmentation. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Takikawa, T., Acuna, D., Jampani, V., and Fidler, S. (2019). Gated-SCNN: Gated Shape CNNs for Semantic Segmentation. arXiv.","DOI":"10.1109\/ICCV.2019.00533"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, C., Yue, P., Di, L., and Wu, Z. (2018). Automatic Identification of Center Pivot Irrigation Systems from Landsat Images Using Convolutional Neural Networks. Agriculture, 8.","DOI":"10.3390\/agriculture8100147"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Saraiva, M., Protas, \u00c9., Salgado, M., and Souza, C. (2020). Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning. Remote Sens., 12.","DOI":"10.3390\/rs12030558"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"De Albuquerque, A.O., de Carvalho J\u00fanior, O.A., Carvalho, O.L.F.D., de Bem, P.P., Ferreira, P.H.G., de Moura, R.D.S., Silva, C.R., Trancoso Gomes, R.A., and Fontes Guimar\u00e3es, R. (2020). Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data. Remote Sens., 12.","DOI":"10.3390\/rs12132159"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"559","DOI":"10.5194\/isprs-annals-V-3-2020-559-2020","article-title":"PVANET-Hough: Detection and location of center pivot irrigation systems from sentinel-2 images","volume":"V-3-2020","author":"Tang","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Baker, N., Lu, H., Erlikhman, G., and Kellman, P.J. (2018). Deep convolutional networks do not classify based on global object shape. PLoS Comput. Biol., 14.","DOI":"10.1371\/journal.pcbi.1006613"},{"key":"ref_16","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., and Brendel, W. (2019). ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv."},{"key":"ref_17","unstructured":"Kim, K.H., Hong, S., Roh, B., Cheon, Y., and Park, M. (2016). PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection. arXiv."},{"key":"ref_18","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7847","DOI":"10.1080\/01431161.2010.531783","article-title":"Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil","volume":"32","author":"Arvor","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","unstructured":"(2021, January 02). Cat\u00e1logo de Metadados da ANA, Available online: https:\/\/metadados.snirh.gov.br\/geonetwork\/srv\/por\/catalog.search#\/metadata\/e2d38e3f-5e62-41ad-87ab-990490841073."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xie, S., and Tu, Z. (2015, January 7\u201313). Holistically-Nested Edge Detection. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.164"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Soria, X., Riba, E., and Sappa, A. (2020, January 1\u20135). Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093290"},{"key":"ref_23","unstructured":"Shang, W., Sohn, K., Almeida, D., and Lee, H. (2016, January 20\u201322). Understanding and improving convolutional neural networks via concatenated rectified linear units. Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kong, T., Yao, A., Chen, Y., and Sun, F. (2016, January 27\u201330). HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.98"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bell, S., Zitnick, C.L., Bala, K., and Girshick, R. (2016, January 27\u201330). Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.314"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.Y.M., Yeh, I.H., Wu, Y.H., Chen, P.Y., and Hsieh, J.W. (2019). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. arXiv.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","unstructured":"Abdi, M., and Nahavandi, S. (2017). Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module. arXiv.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2019). Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. arXiv.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Chun, S., Oh, S.J., Yoo, Y., and Choe, J. (November, January 27). CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chou, C., Chien, J., and Chen, H. (2018, January 12\u201315). Self Adversarial Training for Human Pose Estimation. Proceedings of the 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Honolulu, HI, USA.","DOI":"10.23919\/APSIPA.2018.8659538"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_35","unstructured":"Ghiasi, G., Lin, T.Y., and Le, Q.V. (2018). DropBlock: A regularization method for convolutional networks. arXiv."},{"key":"ref_36","unstructured":"Loshchilov, I., and Hutter, F. (2017). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014). Caffe: Convolutional Architecture for Fast Feature Embedding. Proceedings of the ACM International Conference on Multimedia (MM \u201914), Orlando, FL, USA, 3\u20137 November 2014, ACM Press.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_39","unstructured":"(2021, January 02). Darknet: Open Source Neural Networks in C. Available online: https:\/\/pjreddie.com\/darknet\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/612\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:21:39Z","timestamp":1760160099000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/612"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,9]]},"references-count":39,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13040612"],"URL":"https:\/\/doi.org\/10.3390\/rs13040612","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,2,9]]}}}