{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:08:51Z","timestamp":1770908931983,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T00:00:00Z","timestamp":1531872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFC1405600"],"award-info":[{"award-number":["2017YFC1405600"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671037"],"award-info":[{"award-number":["61671037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Excellence Foundation of BUAA for PhD Students","award":["2017056"],"award-info":[{"award-number":["2017056"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Raft-culture is a way of utilizing water for farming aquatic product. Automatic raft-culture monitoring by remote sensing technique is an important way to control the crop\u2019s growth and implement effective management. This paper presents an automatic pixel-wise raft labeling method based on fully convolutional network (FCN). As rafts are always tiny and neatly arranged in images, traditional FCN method fails to extract the clear boundary and other detailed information. Therefore, a homogeneous convolutional neural network (HCN) is designed, which only consists of convolutions and activations to retain all details. We further design a dual-scale structure (DS-HCN) to integrate higher-level contextual information for accomplishing sea\u2013land segmentation and raft labeling at the same time in a uniform framework. A dataset with Gaofen-1 satellite images was collected to verify the effectiveness of our method. DS-HCN shows a satisfactory performance with a better interpretability and a more accurate labeling result.<\/jats:p>","DOI":"10.3390\/rs10071130","type":"journal-article","created":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T03:50:43Z","timestamp":1531972243000},"page":"1130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4587-7792","authenticated-orcid":false,"given":"Tianyang","family":"Shi","sequence":"first","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"given":"Qizhi","family":"Xu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Zhengxia","family":"Zou","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4772-3172","authenticated-orcid":false,"given":"Zhenwei","family":"Shi","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,18]]},"reference":[{"key":"ref_1","first-page":"41","article-title":"Method to extract raft-cultivation area based on SPOT image","volume":"38","author":"Liu","year":"2013","journal-title":"Sci. Surv. Mapp."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2016.10.008","article-title":"Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features","volume":"123","author":"Wang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fan, J., Chu, J., Geng, J., and Zhang, F. (2015, January 26\u201331). Floating raft aquaculture information automatic extraction based on high resolution SAR images. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326676"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/LGRS.2017.2648641","article-title":"Weighted Fusion-Based Representation Classifiers for Marine Floating Raft Detection of SAR Images","volume":"14","author":"Geng","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"2","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1109\/TGRS.2015.2488681","article-title":"Scene classification via a gradient boosting random convolutional network framework","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5832","DOI":"10.1109\/TGRS.2016.2572736","article-title":"Ship Detection in Spaceborne Optical Image with SVD Networks","volume":"54","author":"Zou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1109\/TIP.2017.2773199","article-title":"Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images","volume":"27","author":"Zou","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3623","DOI":"10.1109\/TGRS.2017.2677464","article-title":"Can a Machine Generate Human-like Language Descriptions for a Remote Sensing Image?","volume":"55","author":"Shi","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pan, B., Shi, Z., and Xu, X. (2017). MugNet: Deep learning for hyperspectral image classification using limited samples. ISPRS J. Photogramm. Remote Sens.","DOI":"10.1016\/j.isprsjprs.2017.11.003"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, S.H., Sun, J., Phillips, P., Zhao, G., and Zhang, Y.D. (2017). Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units. J. Real-Time Image Process.","DOI":"10.1007\/s11554-017-0717-0"},{"key":"ref_12","unstructured":"Wang, Q., Yuan, Z., and Li, X. (2018). GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection. IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s10916-017-0845-x","article-title":"Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling","volume":"42","author":"Wang","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.jocs.2018.05.005","article-title":"Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling","volume":"27","author":"Zhang","year":"2018","journal-title":"J. Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TITS.2017.2749964","article-title":"Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection","volume":"19","author":"Wang","year":"2018","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1109\/TITS.2017.2726546","article-title":"A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling","volume":"19","author":"Wang","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rse.2016.08.023","article-title":"Detecting unburned areas within wildfire perimeters using Landsat and ancillary data across the northwestern United States","volume":"186","author":"Meddens","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rse.2013.06.007","article-title":"Support vector regression and synthetically mixed training data for quantifying urban land cover","volume":"137","author":"Okujeni","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.isprsjprs.2018.04.008","article-title":"l0-based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation","volume":"141","author":"Xu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1109\/TGRS.2017.2669341","article-title":"Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network","volume":"55","author":"Cheng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1109\/LGRS.2017.2727515","article-title":"Fully Convolutional Network with Task Partitioning for Inshore Ship Detection in Optical Remote Sensing Images","volume":"14","author":"Lin","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, H., Shi, Z., and Zou, Z. (2017). Maritime semantic labeling of optical remote sensing images with multi-scale fully convolutional network. Remote Sens., 9.","DOI":"10.3390\/rs9050480"},{"key":"ref_24","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (arXiv, 2015). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs, arXiv."},{"key":"ref_25","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (arXiv, 2014). Striving for simplicity: The all convolutional net, arXiv."},{"key":"ref_26","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1038\/35016072","article-title":"Digital selection and analogue amplification coexist in a cortex-inspiredsilicon circuit","volume":"405","author":"Hahnloser","year":"2000","journal-title":"Nature"},{"key":"ref_28","unstructured":"Clevert, D.A., Unterthiner, T., and Hochreiter, S. (arXiv, 2015). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs), arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (2017, January 21\u201326). Large Kernel Matters\u2013Improve Semantic Segmentation by Global Convolutional Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/JRPROC.1949.232969","article-title":"Communication in the presence of noise","volume":"37","author":"Shannon","year":"1949","journal-title":"Proc. IRE"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1109\/T-AIEE.1928.5055024","article-title":"Certain topics in telegraph transmission theory","volume":"47","author":"Nyquist","year":"1928","journal-title":"Trans. Am. Inst. Electr. Eng."},{"key":"ref_34","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., and Zisserman, A. (2012, March 15). The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. Available online: http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html."},{"key":"ref_35","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv."},{"key":"ref_36","unstructured":"Urban, G., Geras, K.J., Kahou, S.E., Aslan, O., Wang, S., Caruana, R., Mohamed, A., Philipose, M., and Richardson, M. (arXiv, 2016). Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)?, arXiv."},{"key":"ref_37","unstructured":"Bell, S., Lawrence Zitnick, C., Bala, K., and Girshick, R. (July, January 26). Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_38","unstructured":"Kong, T., Yao, A., Chen, Y., and Sun, F. (July, January 26). Hypernet: Towards accurate region proposal generation and joint object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 21\u201326). Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017 (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 11\u201318). Fast R-CNN. Proceedings of the The IEEE International Conference on Computer Vision (ICCV), Las Condes, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cai, Z., Fan, Q., Feris, R.S., and Vasconcelos, N. (2016, January 8\u201316). A unified multi-scale deep convolutional neural network for fast object detection. Proceedings of the European Conference on Computer Vision 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the The IEEE Conference on Computer Vision and Pattern Recognition 2017 (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_44","first-page":"122","article-title":"The OpenCV Library","volume":"120","author":"Bradski","year":"2000","journal-title":"Dr. Dobb\u2019s J. Softw. Tools"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:12:43Z","timestamp":1760195563000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/7\/1130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,18]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2018,7]]}},"alternative-id":["rs10071130"],"URL":"https:\/\/doi.org\/10.3390\/rs10071130","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,18]]}}}