{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T17:31:22Z","timestamp":1776447082625,"version":"3.51.2"},"reference-count":33,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2017,7,1]],"date-time":"2017-07-01T00:00:00Z","timestamp":1498867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Object segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and satellite (or high-resolution, HR) images, has been applied to many application domains, especially in road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts at applying the deep convolutional neural network (DCNN) to extract roads from remote sensing images have been made; however, the accuracy is still limited. In this paper, we present an enhanced DCNN framework specifically tailored for road extraction of remote sensing images by applying landscape metrics (LMs) and conditional random fields (CRFs). To improve the DCNN, a modern activation function called the exponential linear unit (ELU), is employed in our network, resulting in a higher number of, and yet more accurate, extracted roads. To further reduce falsely classified road objects, a solution based on an adoption of LMs is proposed. Finally, to sharpen the extracted roads, a CRF method is added to our framework. The experiments were conducted on Massachusetts road aerial imagery as well as the Thailand Earth Observation System (THEOS) satellite imagery data sets. The results showed that our proposed framework outperformed Segnet, a state-of-the-art object segmentation technique, on any kinds of remote sensing imagery, in most of the cases in terms of precision, recall, and  F 1 .<\/jats:p>","DOI":"10.3390\/rs9070680","type":"journal-article","created":{"date-parts":[[2017,7,3]],"date-time":"2017-07-03T10:27:31Z","timestamp":1499077651000},"page":"680","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Road Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields"],"prefix":"10.3390","volume":"9","author":[{"given":"Teerapong","family":"Panboonyuen","sequence":"first","affiliation":[{"name":"Chulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Rd., Pathumwan, Bangkok 10330, Thailand"}]},{"given":"Kulsawasd","family":"Jitkajornwanich","sequence":"additional","affiliation":[{"name":"Data Science and Computational Intelligence (DSCI) Laboratory, Department of Computer Science, Faculty of Science, King Mongkut\u2019s Institute of Technology Ladkrabang, Chalongkrung Rd., Ladkrabang, Bangkok 10520, Thailand"}]},{"given":"Siam","family":"Lawawirojwong","sequence":"additional","affiliation":[{"name":"Geo-Informatics and Space Technology Development Agency (Public Organization), 120, The Government Complex, Chaeng Wattana Rd., Lak Si, Bangkok 10210, Thailand"}]},{"given":"Panu","family":"Srestasathiern","sequence":"additional","affiliation":[{"name":"Geo-Informatics and Space Technology Development Agency (Public Organization), 120, The Government Complex, Chaeng Wattana Rd., Lak Si, Bangkok 10210, Thailand"}]},{"given":"Peerapon","family":"Vateekul","sequence":"additional","affiliation":[{"name":"Chulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Rd., Pathumwan, Bangkok 10330, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2014.06.006","article-title":"Tensor-Cuts: A simultaneous multi-type feature extractor and classifier and its application to road extraction from satellite images","volume":"95","author":"Poullis","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","unstructured":"Muruganandham, S. (2016). Semantic Segmentation of Satellite Images using Deep Learning. [Master Thesis, Lulea University of Technolog]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2352\/ISSN.2470-1173.2016.10.ROBVIS-392","article-title":"Multiple object extraction from aerial imagery with convolutional neural networks","volume":"2016","author":"Saito","year":"2016","journal-title":"Electron. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 8\u201310). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 13\u201316). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_6","unstructured":"Badrinarayanan, V., Handa, A., and Cipolla, R. (arXiv, 2015). Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling, arXiv."},{"key":"ref_7","unstructured":"Mnih, V. (2013). Machine Learning for Aerial Image Labeling. [Ph.D. Thesis, University of Toronto]."},{"key":"ref_8","unstructured":"Badrinarayanan, V., Kendall, A., and Cipolla, R. (arXiv, 2015). Segnet: A deep convolutional encoder-decoder architecture for image segmentation, arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Volpi, M., and Ferrari, V. (2015, January 8\u201310). Semantic segmentation of urban scenes by learning local class interactions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301377"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2645","DOI":"10.1016\/j.patcog.2015.02.002","article-title":"Detection guided deconvolutional network for hierarchical feature learning","volume":"48","author":"Liu","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_11","unstructured":"Hong, S., Noh, H., and Han, B. (2015). Decoupled deep neural network for semi-supervised semantic segmentation. Adv. Neural Inf. Processing Syst., 1495\u20131503."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.patrec.2016.08.016","article-title":"Using filter banks in convolutional neural networks for texture classification","volume":"84","author":"Andrearczyk","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_14","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","author":"Wang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","unstructured":"Visin, F., Ciccone, M., Romero, A., Kastner, K., Cho, K., Bengio, Y., Matteucci, M., and Courville, A. (July, January 26). Reseg: A recurrent neural network-based model for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, USA."},{"key":"ref_16","unstructured":"Liu, Z., Li, X., Luo, P., Loy, C.C., and Tang, X. (arXiv, 2016). Deep Learning Markov Random Field for Semantic Segmentation, arXiv."},{"key":"ref_17","first-page":"4","article-title":"Efficient inference in fully connected crfs with gaussian edge potentials","volume":"2","author":"Koltun","year":"2011","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (arXiv, 2014). Semantic image segmentation with deep convolutional nets and fully connected crfs, arXiv."},{"key":"ref_19","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (arXiv, 2016). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Audebert, N., Saux, B.L., and Lef\u00e8vre, S. (2017). Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images. Remote Sens., 9.","DOI":"10.3390\/rs9040368"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.neucom.2015.01.054","article-title":"Adaptive road detection via context-aware label transfer","volume":"158","author":"Wang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neucom.2015.05.092","article-title":"Video-based road detection via online structural learning","volume":"168","author":"Yuan","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (arXiv preprint, 2014). Very deep convolutional networks for large-scale image recognition, arXiv preprint."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Y., Qi, H., Dai, J., Ji, X., and Wei, Y. (arXiv, 2016). Fully Convolutional Instance-aware Semantic Segmentation, arXiv.","DOI":"10.1109\/CVPR.2017.472"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (arXiv, 2017). Mask r-cnn, arXiv.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_27","unstructured":"Ioffe, S., and Szegedy, C. (arXiv, 2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift, arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Panboonyuen, T., Vateekul, P., Jitkajornwanich, K., and Lawawirojwong, S. An Enhanced Deep Convolutional Encoder-Decoder Network for Road Segmentation on Aerial Imagery. Recent Advances in Information and Communication Technology Series, Proceedings of International Conference on Computing and Information Technology, Tunis, Tunisia, 27\u201328 April 2017, Springer International Publishing.","DOI":"10.1007\/978-3-319-60663-7_18"},{"key":"ref_29","unstructured":"Kendall, A., Badrinarayanan, V., and Cipolla, R. (arXiv, 2015). Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding, arXiv."},{"key":"ref_30","unstructured":"Kingma, D., and Ba, J. (arXiv, 2014). Adam: A method for stochastic optimization, arXiv."},{"key":"ref_31","unstructured":"Gonzalez, R., and Woods, R. (2008). Digital Image Processing, Prentice Hall."},{"key":"ref_32","unstructured":"McGarigal, K. (2008, December 01). Landscape Metrics for Categorical Map Patterns. Available online: http:\/\/studylib.net\/doc\/7944344\/landscape-metrics-for-categorical-map-patterns."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1080\/01431160802546837","article-title":"Road centreline extraction from high-resolution imagery based on multiscale structural features and support vector machines","volume":"30","author":"Huang","year":"2009","journal-title":"Int. J. 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