{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T04:25:36Z","timestamp":1776745536477,"version":"3.51.2"},"reference-count":21,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2019,5,20]],"date-time":"2019-05-20T00:00:00Z","timestamp":1558310400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IR"],"published-print":{"date-parts":[[2019,5,20]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to study the road segmentation problem of cross-modal remote sensing images.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>First, the baseline network based on the U-net is trained under a large-scale dataset of remote sensing imagery. Then, the cross-modal training data are used to fine-tune the first two convolutional layers of the pre-trained network to achieve the adaptation to the local features of the cross-modal data. For the cross-modal data of different band, an autoencoder is designed to achieve data conversion and local feature extraction.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The experimental results show the effectiveness and practicability of the proposed method. Compared with the ordinary method, the proposed method gets much better metrics.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The originality is the transfer learning strategy that fine-tunes the low-level layers for the cross-modal data application. The proposed method can achieve satisfied road segmentation with a small amount of cross-modal training data, so that is has a good application value. Still, for the similar application of cross-modal data, the idea provided by this paper is helpful.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-05-2018-0112","type":"journal-article","created":{"date-parts":[[2019,1,2]],"date-time":"2019-01-02T03:30:03Z","timestamp":1546399803000},"page":"384-390","source":"Crossref","is-referenced-by-count":11,"title":["Road segmentation of cross-modal remote sensing images using deep segmentation network and transfer learning"],"prefix":"10.1108","volume":"46","author":[{"given":"Hao","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongfang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shicheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2019090415304977700_ref001","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","article-title":"Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks","volume":"130","year":"2017","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"issue":"99","key":"key2019090415304977700_ref002","first-page":"2481","article-title":"SegNet: a deep convolutional encoder-decoder architecture for scene segmentation","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"key2019090415304977700_ref003","article-title":"Fast and accurate deep network learning by exponential linear units (ELUs)","volume-title":"International Conference on Learning Representations","year":"2016"},{"key":"key2019090415304977700_ref004","unstructured":"Dou, Q. Ouyang, C. Chen, C. Chen, H. and Heng, P.-A. (2018), \u201cUnsupervised cross-modality domain adaptation of ConvNets for biomedical image segmentations with adversarial loss\u201d, available at: www.arxiv.org\/abs\/1804.10916"},{"key":"key2019090415304977700_ref005","first-page":"7","article-title":"Cross-modal retrieval with correspondence autoencoder","year":"2014"},{"key":"key2019090415304977700_ref006","first-page":"516","article-title":"Transfer learning for domain adaptation in MRI: application in brain lesion segmentation","volume":"10435","year":"2017","journal-title":"MICCAI"},{"issue":"12","key":"key2019090415304977700_ref007","doi-asserted-by":"crossref","first-page":"4144","DOI":"10.1109\/TGRS.2007.906107","article-title":"Road network extraction and intersection detection from aerial images by tracking road footprints","volume":"45","year":"2007","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"issue":"9","key":"key2019090415304977700_ref008","doi-asserted-by":"crossref","first-page":"907","DOI":"10.3390\/rs9090907","article-title":"Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data","volume":"9","year":"2017","journal-title":"Remote Sensing"},{"key":"key2019090415304977700_ref009","first-page":"448","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","volume-title":"International Conference on Machine Learning","year":"2015"},{"key":"key2019090415304977700_ref010","first-page":"1","article-title":"Adam: a method for stochastic optimization","volume-title":"International Conference on Learning Representations","year":"2015"},{"key":"key2019090415304977700_ref011","doi-asserted-by":"crossref","first-page":"3431","DOI":"10.1109\/CVPR.2015.7298965","article-title":"Fully convolutional networks for semantic segmentation","volume-title":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","year":"2015"},{"key":"key2019090415304977700_ref012","volume-title":"Machine Learning for Aerial Image Labeling","year":"2013"},{"key":"key2019090415304977700_ref013","first-page":"210","article-title":"Learning to detect roads in high-resolution aerial images","volume-title":"European Conference on Computer Vision: Part VI","year":"2010"},{"key":"key2019090415304977700_ref014","first-page":"191","article-title":"An enhanced deep convolutional encoder-decoder network for road segmentation on aerial imagery","volume-title":"Recent Advances in Information and Communication Technology","year":"2017"},{"issue":"7","key":"key2019090415304977700_ref015","article-title":"Road segmentation of remotely-sensed images using deep convolutional neural networks with landscape metrics and conditional random fields","volume":"9","year":"2017","journal-title":"Remote Sensing"},{"key":"key2019090415304977700_ref016","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","volume-title":"Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015","year":"2015"},{"key":"key2019090415304977700_ref017","first-page":"275","volume-title":"Model-Based Road Extraction from Images","year":"1995"},{"key":"key2019090415304977700_ref018","unstructured":"Tsai, Y.-H. Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H. and Chandraker, M. (2018), \u201cLearning to adapt structured output space for semantic segmentation\u201d, available at: www.arxiv.org\/abs\/1802.10349"},{"issue":"12","key":"key2019090415304977700_ref019","doi-asserted-by":"crossref","first-page":"3144","DOI":"10.1080\/01431161.2015.1054049","article-title":"Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine","volume":"36","year":"2015","journal-title":"International Journal of Remote Sensing"},{"issue":"5","key":"key2019090415304977700_ref020","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/LGRS.2017.2672734","article-title":"Road structure refined CNN for road extraction in aerial image","volume":"14","year":"2017","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"7","key":"key2019090415304977700_ref021","doi-asserted-by":"crossref","first-page":"912","DOI":"10.3724\/SP.J.1004.2010.00912","article-title":"A survey of automatic road extraction from remote sensing images","volume":"36","year":"2010","journal-title":"Acta Automatica Sinica"}],"container-title":["Industrial Robot: the international journal of robotics research and application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IR-05-2018-0112\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IR-05-2018-0112\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:39:06Z","timestamp":1753393146000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ir\/article\/46\/3\/384-390\/433819"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,20]]},"references-count":21,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,5,20]]}},"alternative-id":["10.1108\/IR-05-2018-0112"],"URL":"https:\/\/doi.org\/10.1108\/ir-05-2018-0112","relation":{},"ISSN":["0143-991X"],"issn-type":[{"value":"0143-991X","type":"print"}],"subject":[],"published":{"date-parts":[[2019,5,20]]}}}