Analysis of Methods, Challenges, and Perspectives in Rural Road Networks Detection with a Focus on Remote Sensing Images

Document Type : Original Article

Authors

Dep of Geomatics, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran

Abstract

Background and Objective: By advancement of remote sensing and deep learning technologies, the automatic identification of road networks, particularly in rural areas and secondary roads, has become feasible. Moreover, traditional mapping methods, due to their high cost and time-consuming nature, have been increasingly replaced by approaches based on remote sensing data and machine learning. Therefore, this study aims to comprehensively review research conducted on the preparation of road maps using remote sensing data, especially in recent years. In general, preparing a map of the road network involves various methods, and one of the reliable and cost-effective methods is automatic road detection using remote sensing images. Examination of various research results indicates that among the available automatic approaches, methods based on deep learning networks can provide acceptable and more reliable accuracies compared to other conventional methods. Considering the common width used in road construction, remote sensing images with different spatial resolutions have been used in research, each with its advantages and disadvantages. In general, for the type of route under study, which is the rural road network, satellite images with medium spatial resolution and free access cannot achieve high accuracy. Thus, satellite images can be integrated to enhance spatial resolution and improve detection algorithms. One way to integrate satellite images is through super-resolution algorithms. This study can serve as a reference for comparing and selecting methods for the automatic detection of road networks and for improving the spatial resolution of remote sensing images to assist in identifying narrow roads (such as rural road networks), so that researchers can select appropriate data and algorithms based on the objective and type of road network under study.

Materials and Methods: This research aims to investigate the existing methods for road network detection and the utilization of satellite images with medium spatial resolution for this purpose. Initially, the data and methods applicable for generating a road network map were examined. Subsequently, the principles used in the field of road network detection using remote sensing images were described, and based on these principles, classification methods, segmentation, road index, and machine learning were implemented. Methods for improving the spatial resolution of satellite images were also investigated for employing satellite images with medium spatial resolution. Finally, the methods were reviewed in terms of input parameters, mechanism, and output, to identify their strengths and weaknesses and to utilize them optimally for various applications.

Discussion and Analysis: According to the reviews of the examined articles from various Authentic journals in the field of road network detection, classification, segmentation, road index, and machine learning methods account for approximately 28%, 31%, 5%, and 36% shares, respectively. In recent years, classification and segmentation methods based on neural networks have been developed, encompassing a larger share (about 60%) of machine learning methods in general. Additionally, in super-resolution, investigations show that methods based on traditional techniques and deep learning account for approximately 44% and 56% shares, respectively, and most recently, deep learning-based approaches are under development.

Conclusion: The investigations show that using deep learning models in road network detection provides better results than traditional methods and gradually replaces these methods. Deep learning models with the ability to extract complex features and reduce the need for human intervention have improved the accuracy and efficiency of the detection process. On the other hand, super-resolution techniques based on deep learning can solve problems arising from the lack of high-resolution images by increasing the spatial resolution of images. By preserving spectral features and reducing noise, these techniques can provide higher-quality images for road detection. One of the main challenges in road detection from satellite images is the presence of vegetation cover and shadows, which can lead to incomplete and inconsistent detection of roads. To improve this problem, techniques such as tensor voting have been proposed, which can complete and correct roads that have been incompletely detected. Overall, combining super-resolution and deep learning methods for identifying road networks provides a cost-effective and efficient approach to updating road maps. These approaches, by reducing the costs and time required for detection, can be widely used by researchers and professionals in various fields. Furthermore, with further development and improvement of these techniques, new solutions can be developed for road construction, maintenance, and planning.

Keywords

References

Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S. & Alamri, A., 2020a, Deep Learning Approaches Applied to remote Sensing Datasets for Road Extraction: A State-of-the-Art Review, Remote Sensing, 12(9), P. 1444, https://doi.org/10.1109/ ACCESS.2020.3026658.
Abdollahi, A., Pradhan, B. & Alamri, A., 2020b, VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction from High-Resolution Remote Sensing Data, IEEE Access, 8, PP. 179424-179436, https://doi.org/10.1109/ACCESS.2020. 3026658.
Ablin, R., Sulochana, C.H. & Prabin, G., 2020, An Investigation in Satellite Images Based on Image Enhancement Techniques, European Journal of Remote Sensing, 53(sup2), PP. 86-94, https://doi.org/10.1080/22797254. 2019. 1673216.
Adigun, O., Olsen, P.A. & Chandra, R., 2022, Location Aware Super-Resolution for Satellite Data Fusion, IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium.
Ahmed, M.W., Saadi, S. & Ahmed, M., 2022, Automated Road Extraction Using Reinforced Road Indices for Sentinel-2 Data, Array, 16, P. 100257, https://doi.org/10.1016/j.array.2022.100257.
Alvarez-Vanhard, E., Corpetti, T. & Houet, T., 2021, UAV & Satellite Synergies for Optical Remote Sensing Applications: A Literature Review, Science of Remote Sensing, 3, P. 100019, https://doi.org/ 10.1016/j.srs.2021.100019.
Arora, S., Suman, H.K., Mathur, T., Pandey, H.M. & Tiwari, K., 2023, Fractional Derivative Based Weighted Skip Connections for Satellite Image Road Segmentation, Neural Networks, 161, PP. 142-153, https://doi.org/10.1016/j.neunet. 2023.01.031.
Babaali, K.O., Zigh, E., Djebbouri, M. & Kadiri, M., 2014, Survey of Some New Road Extraction Methods, The International Journal Of Engineering And Science (IJES), 3(11), PP. 28-33.
Badran, A., El-Geneidy, A. & Miranda-Moreno, L., 2024, A Review of Techniques to Extract Road Network Features from Global Positioning System Data for Transport Modelling, Transport Reviews, 44(1), PP. 69-84, https://doi.org/10.1080/ 01441647.2023.2229521.
 
Bakhtiari, H.R.R., Abdollahi, A. & Rezaeian, H., 2017, Semi Automatic Road Extraction from Digital Images, The Egyptian Journal of Remote Sensing and Space Science, 20(1), PP. 117-123, https://doi.org/10.1016/ j.ejrs.2017.03.001.
Belgiu, M. & Stein, A., 2019, Spatiotemporal Image Fusion in Remote Sensing, Remote Sensing, 11(7), P. 818, https://doi.org/ 10.3390/rs11070818.
Bevilacqua, M., Roumy, A., Guillemot, C. & Alberi-Morel, M.L., 2012, Low-Complexity Single-Image Super-Resolution Based on Nonnegative Neighbor Embedding, https://doi.org/10.5244/C.26.135.
Blaschke, T., Burnett, C. & Pekkarinen, A., 2004, Image Segmentation Methods for Object-Based Analysis and Classification, Remote Sensing Image Analysis: Including the Spatial Domain, 5, PP. 211-236.
Botelho, J. Jr., Costa, S.C., Ribeiro, J.G. & Souza, C.M. Jr., 2022, Mapping Roads in the Brazilian Amazon with Artificial Intelligence and Sentinel-2, Remote Sensing, 14(15), P. 3625, https://doi.org/ 10.3390/rs14153625.
Chang, H., Yeung, D.-Y. & Xiong, Y., 2004, Super-Resolution through Neighbor Embedding, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, CVPR 200.
Chen, L., Letu, H., Fan, M., Shang, H., Tao, J., Wu, L., Zhang, Y., Yu, C., Gu, J. & Zhang, N., 2022a, An Introduction to the Chinese High-Resolution Earth Observation System: Gaofen-1~ 7 Civilian Satellites, Journal of Remote Sensing, https://doi.org/ 10.34133/2022/976953.
Chen, W., Ouyang, S., Yang, J., Li, X., Zhou, G. & Wang, L., 2022b, JAGAN: A Framework for Complex Land Cover Classification Using Gaofen-5 AHSI Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, PP. 1591-1603, https://doi.org/ 10.1109/JSTARS.2022.3144339.
Chen, Z., Deng, L., Luo, Y., Li, D., Junior, J.M., Gonçalves, W.N., Nurunnabi, A.A.M., Li, J., Wang, C. & Li, D., 2022c, Road Extraction in remote Sensing Data: A Survey, International Journal of Applied Earth Observation and Geoinformation, 112, P. 102833, https://doi.org/10.1016/ j.jag.2022.102833.
Chen, Y., Xia, R., Yang, K. & Zou, K., 2023a, MFFN: Image Super-Resolution via Multi-Level Features Fusion Network, The Visual Computer, 40, PP. 489-504, https://doi.org/10.1007/s00371-023-02795-0.
Chen, G., Lu, H., Zou, W., Li, L., Emam, M., Chen, X., Jing, W., Wang, J. & Li, C., 2023b, Spatiotemporal Fusion for Spectral Remote Sensing: A Statistical Analysis and Review, Journal of King Saud University-Computer and Information Sciences, https://doi.org/ 10.1016/j.jksuci.2023.02.021.
Daihong, J., Sai, Z., Lei, D. & Yueming, D., 2022, Multi-Scale Generative Adversarial Network for Image Super-Resolution, Soft Computing, 26(8), PP. 3631-3641, https://doi.org/10.1007/s00500-022-06822-5.
Deepan, P., Abinaya, S., Haritha, G. & Iswarya, V., 2018, Road Recognition from Remote Sensing Imagery Using Machine Learning, International Research Journal of Engineering and Technology, 5(3), PP. 3677-3683.
Dick, A., Raynaud, J.-L., Rolland, A., Pelou, S., Coustance, S., Dedieu, G., Hagolle, O., Burochin, J.-P., Binet, R. & Moreau, A., 2022, Venμs: Mission Characteristics, Final Evaluation of the First Phase and Data Production, Remote sensing, (14)14, P. 3281, https://doi.org/10.3390/rs14143281.
Dong, J., Zhuang, D., Huang, Y. & Fu, J., 2009, Advances in Multi-Sensor Data Fusion: Algorithms and Applications, Sensors, 9(10), PP. 7771-7784, https://doi.org/10.3390/ s91007771.
Dong, W., Zhang, L., Shi, G. & Wu, X., 2011, Mage Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization, IEEE Transactions on Image Processing, 20(7), PP. 1838-1857, https://doi.org/10.1109/TIP. 2011.2108306.
Dong, C., Loy, C.C., He, K. & Tang, X., 2014, Learning a Deep Convolutional Network for Image Super-Resolution, Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13.
Emad, M., Peemen, M. & Corporaal, H., 2021, Dualsr: Zero-Shot Dual Learning for Real-World Super-Resolution, Proceedings of the IEEE/CVF winter conference on applications of computer vision.
Freedman, G. & Fattal, R., 2011, Image and Video Upscaling from Local Self-Examples, ACM Transactions on Graphics (TOG), 30(2), PP. 1-11, https://doi.org/ 10.1145/1944846.1944852.
Freeman, W.T., Jones, T.R. & Pasztor, E.C., 2002, Example-Based Super-Resolution, IEEE Computer Graphics and Applications, 22(2), PP. 56-65, https://doi.org/10.1109/ 38.988747.
Galar Idoate, M., Sesma Redín, R., Ayala Lauroba, C. & Aranda, C., 2019, Super-Resolution for Sentinel-2 Images, International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives, 2019, XLII-2/W16, PP. 95-102, https://doi.org/ 10.5194/isprs-archives-XLII-2-W16-95-2019.
Gao, X., Zhang, K., Tao, D. & Li, X., 2012, Image Super-Resolution with Sparse Neighbor Embedding, IEEE Transactions on Image Processing, 21(7), PP. 3194-3205, https://doi.org/10.1109/TIP.2012.2190080.
Gao, L., Song, W., Dai, J. & Chen, Y., 2019, Road Extraction from High-Resolution Remote Sensing Imagery Using Refined Deep Residual Convolutional Neural Network, Remote Sensing, 11(5), P. 552, https://doi.org/10.3390/rs10091461.
Gatys, L.A., Ecker, A.S. & Bethge, M., 2015, A Neural Algorithm of Artistic Style, arXiv preprint arXiv:1508.06576, https://doi.org/ 10.48550/arXiv.1508.06576.
Ghandorh, H., Boulila, W., Masood, S., Koubaa, A., Ahmed, F. & Ahmad, J., 2022, Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images, Remote Sensing, 14(3), P. 613, https://doi.org/ 10.3390/rs14030613.
Ghassemian, H., 2016, A Review of Remote Sensing Image Fusion Methods, Information Fusion, 32, PP. 75-89, https://doi.org/ 10.1016/j.inffus.2016.03.003.
Gonzalez, R.C. & Woods, R.E., 2006, Digital Image Processing, Pearson Education, https://books.google.com/books?id=MaYuAAAAQBAJ.
Grinias, I., Panagiotakis, C. & Tziritas, G., 2016, MRF-Based Segmentation and Unsupervised Classification for Building and Road Detection in Peri-Urban Areas of High-Resolution Satellite Images, ISPRS Journal of Photogrammetry and Remote Sensing, 122, PP. 145-166, https://doi.org/ 10.1016/j.isprsjprs.2016.10.010.
He, H. & Siu, W.-C., 2011, Single Image Super-Resolution Using Gaussian Process Regression, CVPR 2011, Colorado Springs, CO, USA, PP. 449-456, https://doi.org/ 10.1109/CVPR.2011.5995713.
Henry, C., Azimi, S.M. & Merkle, N., 2018, Road Segmentation in SAR Satellite Images with Deep Fully Convolutional Neural Networks, IEEE Geoscience and Remote Sensing Letters, 15(12), PP. 1867-1871.
Huang, J.-B., Singh, A. & Ahuja, N., 2015, Single Image Super-Resolution from Transformed Self-Exemplars, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, PP. 5197-5206, https://doi.org/10.1109/ CVPR.2015.7299156.
Irani, M. & Peleg, S., 1991, Improving resolution by Image Registration, CVGIP: Graphical Models and Image Processing, 53(3), PP. 231-239, https://doi.org/ 10.1016/1049-9652(91)90045-L.
Javan, F.D., Samadzadegan, F., Mehravar, S., Toosi, A., Khatami, R. & Stein, A., 2021, A Review of Image Fusion Techniques for Pan-Sharpening of High-Resolution Satellite Imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 171, PP. 101-117, https://doi.org/10.1016/j. isprsjprs.2020.11.001.
Jia, J., Sun, H., Jiang, C., Karila, K., Karjalainen, M., Ahokas, E., Khoramshahi, E., Hu, P., Chen, C. & Xue, T., 2021, Review on Active and Passive Remote Sensing Techniques for Road Extraction, Remote Sensing, 13(21), P. 4235, https://doi.org/ 10.3390/rs13214235.
Jing, J., Liu, S., Wang, G., Zhang, W. & Sun, C., 2022, Recent Advances on Image Edge Detection: A Comprehensive Review, Neurocomputing, 503, PP. 259-271, https://doi.org/https://doi.org/10.1016/j.neucom.2022.06.083.
Jozdani, S.E., Johnson, B.A. & Chen, D., 2019, Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification, Remote Sensing, 11(14), P. 1713, https://doi.org/10.3390/rs11141713.
Jurado, J.M., López, A., Pádua, L. & Sousa, J.J., 2022, Remote Sensing Image Fusion on 3D Scenarios: A Review of Applications for Agriculture and Forestry, International Journal of Applied Earth Observation and Geoinformation, 112, P. 102856, https://doi.org/10.1016/j.jag.2022. 102856.
Kahraman, I., Karas, I. & Akay, A.E., 2018, Road Extraction Techniques from Remote Sensing Images: A Review, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, PP. 339-342, https://doi.org/10.5194/isprs-archives-XLII-4-W9-339-2018.
Kiani, A. & Ebadi, H., 2015, Development of a New Method for Edge Detection from High-Resolution Aerial/Satellite Images, with Emphasis on Threshold Optimization and Using Imperialist Competitive Algorithm [Research], Journal of Geomatics Science and Technology, 4(4), PP. 67-82, https://doi.org/ http://jgst.issgeac.ir/article-1-308-en.html.
Kiani, A. & Sahebi, M.R., 2015, Edge Detection Based on the Shannon Entropy by Piecewise Thresholding on Remote Sensing Images, IET Computer Vision, 9(5), PP. 758-768, https://doi.org/10.1049/ iet-cvi.2013.0192.
Kiani, A., Ebadi, H. & Farnood Ahmadi, F., 2019a, Development of An Object-Based Interpretive System Based on Weighted Scoring Method in a Multi-Scale Manner, ISPRS International Journal of Geo-Information, 8(9), P. 398, https://doi.org/ 10.3390/ijgi8090398.
Kiani, A., Ebadi, H. & Khanlou, H.A., 2019b, Object Based Interpretation of High Spatial Remote Sensing Images Based on Knowledge-Based Systems, Iranian Journal of Remote Sensing & GIS, 10(4), PP. 27-54, https://gisj.sbu.ac.ir/article_96622_bf8570bd278dd917e7ffa46f870cfc82.pdf.
Kim, K.I. & Kwon, Y., 2010, Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(6), PP. 1127-1133, https://doi.org/10.1109/TPAMI.2010.25.
Kim, J., Lee, J.K. & Lee, K.M., 2016, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, https://doi.org/ 10.48550/arXiv.1511.04587.
Kim, G., Park, J., Lee, K., Lee, J., Min, J., Lee, B., Han, D.K. & Ko, H., 2020, Unsupervised Real-World Super Resolution with Cycle Generative Adversarial Network and Domain Discriminator, IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 2020, PP. 1862-1871, https://doi.org/ 10.1109/CVPRW50498.2020.00236.
Köhler, S., Wojcik, M., Xu, K. & Dernburg, A.F., 2020, Dynamic Molecular Architecture of the Synaptonemal Complex, BioRxiv, https://doi.org/10.1101/ 2020.02.16.947804 .
Lan, R., Sun, L., Liu, Z., Lu, H., Pang, C. & Luo, X., 2020, MADNet: A Fast and Lightweight Network for Single-Image Super Resolution, IEEE Transactions on Cybernetics, 51(3), PP. 1443-1453, https://doi.org/10.1109/TCYB.2020.2970104 .
Lanaras, C., Bioucas-Dias, J., Galliani, S., Baltsavias, E. & Schindler, K., 2018, Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network, ISPRS Journal of Photogrammetry and Remote Sensing, 146, PP. 305-319, https://doi.org/10.1016/ j.isprsjprs.2018.09.018.
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J. & Wang, Z., 2017, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, PP. 105-114, https://doi.org/10.1109/CVPR.2017.19.
Li, S., Kang, X., Fang, L., Hu, J. & Yin, H., 2017, Pixel-Level Image Fusion: A Survey of the State of the Art, Information Fusion, 33, PP. 100-112, https://doi.org/ 10.1016/j.inffus.2016.05.004.
Li, J., Li, Y., He, L., Chen, J. & Plaza, A., 2020, Spatio-Temporal Fusion for Remote Sensing Data: An Overview and New Benchmark, Science China Information Sciences, 63, PP. 1-17, https://doi.org/ 10.1007/s11432-019-2785-y.
Lian, R., Wang, W., Mustafa, N. & Huang, L., 2020, Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, PP. 5489-5507, https://doi.org/ 10.1109/ JSTARS.2020.3023549.
Lim, B., Son, S., Kim, H., Nah, S. & Mu Lee, K., 2017, Enhanced Deep Residual Networks for Single Image Super-Resolution, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA, PP. 1132-1140, https://doi.org/ 10.1109/CVPRW.2017.151.
Liu, W. & Wang, H., 2008, An Interactive Image Segmentation Method Based on Graph Theory, J. Electron. Inf. Technol, 8(30), PP. 1973-1976, https://doi.org/ 10.3390/s23146394.
Liu, Y., Yao, J., Lu, X., Xia, M., Wang, X. & Liu, Y., 2018, RoadNet: Learning to Comprehensively Analyze Road Networks in Complex Urban Scenes from High-Resolution Remotely Sensed Images, IEEE Transactions on Geoscience and Remote Sensing, 57(4), PP. 2043-2056, https://doi.org/10.1109/TGRS.2018.2870871.
Liu, P., Wang, Q., Yang, G., Li, L. & Zhang, H., 2022, Survey of Road Extraction Methods in Remote Sensing Images Based on Deep Learning, PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 90(2), PP. 135-159, https://doi.org/10.1007/ s41064-022-00194-z.
Liu, R., Wu, J., Lu, W., Miao, Q., Zhang, H., Liu, X., Lu, Z. & Li, L., 2024, A Review of Deep Learning-Based Methods for Road Extraction from High-Resolution Remote Sensing Images, Remote Sensing, 16(12), P. 2056, https://doi.org/10.3390/rs16122056.
Luo, Z., Zhou, K., Tan, Y., Wang, X., Zhu, R. & Zhang, L., 2023, AD-RoadNet: An Auxiliary-Decoding Road Extraction Network Improving Connectivity While Preserving Multiscale Road Details, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, https://doi.org/10.1109/JSTARS.2023.3289583.
Market Analysis News Site, 1400, https://www.tahlilbazaar.com/news/129756/.
Martin, D., Fowlkes, C., Tal, D. & Malik, J., 2001, A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics, Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001.
Masek, J.G., Wulder, M.A., Markham, B., McCorkel, J., Crawford, C.J., Storey, J. & Jenstrom, D.T., 2020, Landsat 9: Empowering Open Science and Applications through Continuity, Remote Sensing of Environment, 248, P. 111968, https://doi.org/10.1016/j.rse.2020.111968.
Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T. & Aizawa, K., 2017, Sketch-Based Manga Retrieval Using Manga109 Dataset, Multimedia Tools and Applications, 76, PP. 21811-21838, https://doi.org/10.48550/arXiv. 1510.04389.
Michel, J., Vinasco-Salinas, J., Inglada, J. & Hagolle, O., 2022, SEN2VENµS, a Dataset for the Training of Sentinel-2 Super-Resolution Algorithms, Data, 7(7), P. 96, https://doi.org/10.3390/data7070096.
Mnih, V., 2013, Machine Learning for Aerial Image Labeling, University of Toronto (Canada).
Patnaik, A., Bhuyan, M. & MacDorman, K.F., 2024, A Two-Branch Multi-Scale Residual Attention Network for Single Image Super-Resolution in Remote Sensing Imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, https://doi.org/10.1109/ JSTARS.2024.3371710.
Pohl, C. & Van Genderen, J.L., 1998, Review Article Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications, International Journal of Remote Sensing, 19(5), PP. 823-854, https://doi.org/10.1080/014311698215748.
Polatkan, G., Zhou, M., Carin, L., Blei, D. & Daubechies, I., 2014, A Bayesian Nonparametric Approach to Image Super-Resolution, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(2), PP. 346-358, https://doi.org/10.1109/ TPAMI.2014.2321404.
Pruthi, J. & Dhingra, S., 2023, A Review of Research on Road Feature Extraction Through Remote Sensing Images Based on Deep Learning Algorithms, 3rd International Conference on Innovative Sustainable Computational Technologies (CISCT), 8-9th Sept., https://doi.org/ 10.1109/CISCT57197.2023.10351299.
Purkait, P. & Chanda, B., 2013, Image Upscaling Using Multiple Dictionaries of Natural Image Patches, Asian Conference on Computer Vision, https://doi.org/ 10.1007/978-3-642-37431-9_22.
Reddy, S.L.K., Rao, C.V., Kumar, P.R., Anjaneyulu, R.V.G. & Bothale, V.M., 2019, Automatic Road Feature Extraction Using MRF from LANDSAT-8 OLI Images, 2019 IEEE Recent Advances in Geoscience and Remote Sensing: Technologies, Standards and Applications (TENGARSS), Kochi, India, 2019, PP. 15-20, https://doi.org/10.1109/TENGARSS48957.2019.8976046.
Sahu, D.K. & Parsai, M., 2012, Different Image Fusion Techniques–A Critical Review, International Journal of Modern Engineering Research (IJMER), 2(5), PP. 4298-4301.
Shahi, K., Shafri, H.Z., Taherzadeh, E., Mansor, S. & Muniandy, R., 2015, A Novel Spectral Index to Automatically Extract Road Networks from WorldView-2 Satellite Imagery, The Egyptian Journal of Remote Sensing and Space Science, 18(1), PP. 27-33, https://doi.org/10.1016/j.ejrs.2014.12.003.
Shao, Z., Zhou, Z., Huang, X. & Zhang, Y., 2021, MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images, Remote Sensing, 13(2), P. 239, https://doi.org/10.3390/rs13020239.
Sharma, P., Kumar, R. & Gupta, M., 2023, Remote Sensing Images based Road Network Extraction Using Deep Learning: A Systematic Review, Research Square Platform LLC, https://dx.doi.org/ 10.21203/rs.3.rs-2493427/v1.
Singh, P.P. & Garg, R.D., 2014, Road Detection from Remote Sensing Images Using Impervious Surface Characteristics: Review and Implication, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, PP. 955-959, https://doi.org/10.5194/ isprsarchives-XL-8-955-2014.
Singh, S., Mittal, N. & Singh, H., 2021, Review of Various Image Fusion Algorithms and Image Fusion Performance Metric, Archives of Computational Methods in Engineering, 28, PP. 3645-3659, https://doi.org/10.1007/s11831-020-09518-x.
Singh, S., Singh, H., Bueno, G., Deniz, O., Singh, S., Monga, H., Hrisheekesha, P. & Pedraza, A., 2023, A Review of Image Fusion: Methods, Applications and Performance Metrics, Digital Signal Processing, 137, P. 104020, https://doi.org/ 10.1016/j.dsp.2023.104020.
Sonka, M., Hlavac, V. & Boyle, R., 2014, Image Processing, Analysis, and Machine Vision, Cengage Learning.
Soufi, O. & Belouadha, F.Z., 2023, FSRSI: New Deep Learning-Based Approach for Super-Resolution of Multispectral Satellite Images, Ingenierie des Systemes d'Information, 28(1), P. 113, https://doi.org/ 10.18280/isi.280112.
Spoto, F., Sy, O., Laberinti, P., Martimort, P., Fernandez, V., Colin, O., Hoersch, B. & Meygret, A., 2012, Overview of Sentinel-2, IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, PP. 1707-1710, https://doi.org/10.1109/ IGARSS.2012.6351195.
Sun, J., Xu, Z. & Shum, H.-Y., 2008, Image Super-Resolution Using Gradient Profile Prior, IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, PP. 1-8, https://doi.org/10.1109/CVPR. 2008.4587659.
Tao, C., Qi, J., Li, Y., Wang, H. & Li, H., 2019, Spatial Information Inference Net: Road Extraction Using Road-Specific Contextual Information, ISPRS Journal of Photogrammetry and Remote Sensing, 158, PP. 155-166, https://doi.org/10.1016/ j.isprsjprs.2019.10.001.
Timofte, R., De Smet, V. & Van Gool, L., 2013, Anchored Neighborhood Regression for Fast Example-Based Super-Resolution, 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia, PP. 1920-1927, https://doi.org/10.1109/ICCV. 2013.241.
Timofte, R., Agustsson, E., Van Gool, L., Yang, M.-H. & Zhang, L., 2017, Ntire 2017 Challenge on Single Image Super-Resolution: Methods and Results, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA, PP. 1110-1121, https://doi.org/10.1109/CVPRW.2017.149.
Versaci, M. & Morabito, F.C., 2021, Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence, International Journal of Fuzzy Systems, 23(4), PP. 918-936, https://doi.org/10.1007 /s40815-020-01030-5.
Wang, W., Yang, N., Zhang, Y., Wang, F., Cao, T. & Eklund, P., 2016, A Review of Road Extraction from Remote Sensing Images, Journal of Traffic and Transportation Engineering (English Ed.), 3(3), PP. 271-282, https://doi.org/10.1016/j.jtte.2016.05.005.
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y. & Change Loy, C., 2018, Esrgan: Enhanced Super-Resolution Generative Adversarial Networks, Proceedings of the European Conference on Computer Vision (ECCV) Workshops.
Wei, Y., Zhang, K. & Ji, S., 2019, Road Network Extraction from Satellite Images Using CNN Based Segmentation and Tracing, IGARSS 2019, IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, PP. 3923-3926, https://doi.org/10.1109/ IGARSS.2019.8898565.
Winiwarter, L., Coops, N.C., Bastyr, A., Roussel, J.-R., Zhao, D.Q., Lamb, C.T. & Ford, A.T., 2024, Extraction of Forest Road Information from CubeSat Imagery Using Convolutional Neural Networks, Remote Sensing, 16(6), P. 1083, https://doi.org/10.3390/rs16061083.
Xia, G.-S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L. & Lu, X., 2017, AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification, IEEE Transactions on Geoscience and Remote Sensing, 55(7), PP. 3965-3981, https://doi.org/10.1109/TGRS.2017.2685945.
Xie, J., Xu, L. & Chen, E., 2012, Image Denoising and Inpainting with Deep Neural Networks, NIPS'12: Proceedings of the 26th International Conference on Neural Information Processing Systems, 1, PP. 341-343, https://dl.acm.org/doi/10.5555/ 2999134.2999173.
Yadav, M., 2021, A Multi-Constraint Combined Method for Road Extraction from Airborne Laser Scanning Data, Measurement, 186, P. 110077, https://doi.org/ 10.1016/j.measurement.2021.110077.
Yang, J., Wright, J., Huang, T.S. & Ma, Y., 2010, Image Super-Resolution via Sparse Representation, IEEE Transactions on Image Processing, 19(11), PP. 2861-2873, https://doi.org/10.1109/TIP.2010.2050625.
Yanuargi, B. & Utami, E., 2022, Convolutional Neural Network for Road Network Detections Using Sentinel 2A, International Journal of Innovative Science and Research Technology, 7(12), PP. 463-468, https://doi.org/10.5281/zenodo.7487943.
Ye, W., Lin, B., Lao, J., Liu, Y. & Lin, Z., 2024, MRA-IDN: A Lightweight Super-Resolution Framework of Remote Sensing Images Based on Multi-Scale Residual Attention Fusion Mechanism, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, PP. 7781-7800, https://doi.org/10.1109/JSTARS. 2024.3381653.
Zabalza, M. & Bernardini, A., 2022, Super-Resolution of Sentinel-2 Images Using a Spectral Attention Mechanism, Remote Sensing, 14(12), P. 2890, https://doi.org/ doi.org/10.3390/rs14122890.
Zeyde, R., Elad, M. & Protter, M., 2012, On Single Image Scale-Up Using Sparse-Representations, Curves and Surfaces: 7th International Conference, Avignon, France, June 24-30, Revised Selected Papers 7, https://doi.org/10.1007/978-3-642-27413-8_47.
Zhang, Y., 2008, Methods for Image Fusion Quality Assessment-A Review, Comparison and Analysis, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(PART B7), 1101-1109.
Zhang, J., 2010, Multi-Source Remote Sensing Data Fusion: Status and Trends, International Journal of Image and Data Fusion, 1(1), PP. 5-24, https://doi.org/ 10.1080/19479830903561035.
Zhang, K., Gao, X., Tao, D. & Li, X., 2012, Single Image Super-Resolution with Non-Local Means and Steering Kernel Regression, IEEE Transactions on Image Processing, 21(11), PP. 4544-4556, https://doi.org/10.1109/TIP.2012.2208977.
Zhang, Z., Liu, Q. & Wang, Y., 2018, Road Extraction by Deep Residual U-Net, IEEE Geoscience and Remote Sensing Letters, 15(5), PP. 749-753.
Zhang, Q., Kong, Q., Zhang, C., You, S., Wei, H., Sun, R. & Li, L., 2019a, A New Road Extraction Method Using Sentinel-1 SAR Images Based on the Deep Fully Convolutional Neural Network, European Journal of Remote Sensing, 52(1), PP. 572-582, https://doi.org/10.1080/22797254.2019. 1694447.
Zhang, C., Wei, S., Ji, S. & Lu, M., 2019b, Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification, ISPRS International Journal of Geo-Information, 8(4), P. 189, https://doi.org/10.3390/ijgi8040189.
Zhang, K., Sumbul, G. & Demir, B., 2020, An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks, Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia, 2020, PP. 69-72, https://doi.org/ 10.1109/M2GARSS47143.2020.9105165.
Zhang, T., Su, J., Xu, Z., Luo, Y. & Li, J., 2021, Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier, Applied Sciences, 11(2), P. 543, https://doi.org/ 10.3390/app11020543.
Zhang, Y., Dong, L., Yang, H., Qing, L., He, X. & Chen, H., 2022,Weakly-Supervised Contrastive Learning-Based Implicit Degradation Modeling for Blind Image Super-Resolution, Knowledge-Based Systems, 249, P. 108984, https://doi.org/ 10.1016/j.knosys.2022.108984.
 Zhang, Y., Zhang, L., Wang, Y. & Xu, W., 2024a, AGF-Net: Adaptive Global Feature Fusion Network for Road Extraction from Remote-Sensing Images, Complex & Intelligent Systems, 10, PP. 4311-4328, https://doi.org/10.1007/ s40747-024-01364-9.
Zhang, W., Tan, Z., Lv, Q., Li, J., Zhu, B. & Liu, Y., 2024b, An Efficient Hybrid CNN-Transformer Approach for Remote Sensing Super-Resolution, Remote Sensing, 16(5), P. 880, https://doi.org/ 10.3390/rs16050880.
Zhao, N., Wei, Q., Basarab, A., Dobigeon, N., Kouame, D. & Tourneret, J.-Y., 2015, Fast Single Image Super-Resolution, arXiv preprint arXiv:1510.00143, https://doi.org/ 10.1109/TIP.2016.2567075.
Zhu, X., Cai, F., Tian, J. & Williams, T.K.-A., 2018, Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions, Remote Sensing, 10(4), P. 527, https://doi.org/ 10.3390/rs10040527.
Zhu, Q., Zhang, Y., Wang, L., Zhong, Y., Guan, Q., Lu, X., Zhang, L. & Li, D., 2021, A Global Context-Aware and Batch-Independent Network for Road Extraction from VHR Satellite Imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 175, PP. 353-365, https://doi.org/ 10.1016/j.isprsjprs. 2021.03.016.
Zhu, X., Huang, X., Cao, W., Yang, X., Zhou, Y. & Wang, S., 2024, Road Extraction from Remote Sensing Imagery with Spatial Attention Based on Swin Transformer, Remote Sensing, 16(7), P. 1183, https://doi.org/10.3390/rs16071183.
Zou, Q., Ni, L., Zhang, T. & Wang, Q., 2015, Deep Learning Based Feature Selection for Remote Sensing Scene Classification, IEEE Geoscience and Remote Sensing Letters, 12(11), PP. 2321-2325, https://doi.org/10.1109/LGRS.2015.2475299.
 

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