Satellite IoT Based Road Extraction from VHR Images Through Superpixel-CNN Architecture

Deren, 2015, On construction of China's space information network, Geomat. Inf. Sci. Wuhan Univ., 40, 711 Lee, 2015, Geospatial Big Data: challenges and opportunities, Big Data Res., 2, 74, 10.1016/j.bdr.2015.01.003 Lovelly, 2014, A framework to analyze processor architectures for next-generation on-board space computing, 1 Rho, 2018, Social Internet of Things: applications, architectures and protocols, Future Gener. Comput. Syst., 82, 667, 10.1016/j.future.2018.01.035 Afzal, 2019, Enabling IoT platforms for social IoT applications: vision, feature mapping, and challenges, Future Gener. Comput. Syst., 92, 718, 10.1016/j.future.2017.12.002 Neilson, 2019, Systematic review of the literature on Big Data in the transportation domain: concepts and applications, Big Data Res., 17, 35, 10.1016/j.bdr.2019.03.001 Behera, 2022, Vegetation extraction from uav-based aerial images through deep learning, Comput. Electron. Agric., 198, 10.1016/j.compag.2022.107094 Al-Jarrah, 2015, Efficient machine learning for Big Data: a review, Big Data Res., 2, 87, 10.1016/j.bdr.2015.04.001 Krizhevsky, 2017, ImageNet classification with deep convolutional neural networks, Commun. ACM, 60, 84, 10.1145/3065386 Simonyan, 2015, Very deep convolutional networks for large-scale image recognition He, 2016, Deep residual learning for image recognition, 770 Girshick, 2016, 1440 Redmon, 2016, You only look once: unified, real-time object detection, 779 Wang, 2015, Visual tracking with fully convolutional networks, 3119 Shelhamer, 2017, Fully convolutional networks for semantic segmentation, IEEE Trans. Image Process., 39, 640 Szegedy, 2017, Inception-v4, inception-ResNet and the impact of residual connections on learning, 4278 Myint, 2011, Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery, Remote Sens. Environ., 115, 1145, 10.1016/j.rse.2010.12.017 Fukushima, 1982, Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition, 267 LeCun, 1989, Backpropagation applied to handwritten zip code recognition, Neural Comput., 1, 541, 10.1162/neco.1989.1.4.541 Bacco, 2019, IoT applications and services in space information networks, IEEE Wirel. Commun., 26, 31, 10.1109/MWC.2019.1800297 Wei, 2019, Application of edge intelligent computing in satellite Internet of things, 85 Cao, 2019, Space-based cloud-fog computing architecture and its applications, 166 Wang, 2019, Satellite edge computing for the Internet of things in aerospace, Sensors, 19, 10.3390/s19204375 Jan, 2019, Deep learning in big data analytics: a comparative study, Comput. Electr. Eng., 75, 275, 10.1016/j.compeleceng.2017.12.009 Al-Turjman, 2019, 5G-enabled devices and smart-spaces in social-IoT: an overview, Future Gener. Comput. Syst., 92, 732, 10.1016/j.future.2017.11.035 Roopa, 2020, Dynamic management of traffic signals through social IoT, Proc. Comput. Sci., 171, 1908, 10.1016/j.procs.2020.04.204 Behera, 2021, Aerial data aiding smart societal reformation: current applications and path ahead, IEEE IT Prof., 23, 82, 10.1109/MITP.2020.3020433 Swain, 2021, METO: matching-theory-based efficient task offloading in IoT-Fog interconnection networks, IEEE Int. Things J., 8, 12705, 10.1109/JIOT.2020.3025631 Hedman, 2010, Road network extraction in VHR SAR images of urban and suburban areas by means of class-aided feature-level fusion, IEEE Trans. Geosci. Remote Sens., 48, 1294, 10.1109/TGRS.2009.2025123 Li, 2015, Superpixel segmentation using linear spectral clustering, 1356 Davis, 1975, Region extraction by averaging and thresholding, IEEE Trans. Syst. Man Cybern., 383, 10.1109/TSMC.1975.5408419 Saati, 2015, A method for automatic road extraction of high resolution SAR imagery, J. Indian Soc. Remote, 43, 697, 10.1007/s12524-015-0454-4 Zhong, 2015, Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery, IEEE Trans. Geosci. Remote Sens., 53, 6207, 10.1109/TGRS.2015.2435801 Maboudi, 2018, Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images, ISPRS J. Photogramm. Remote Sens., 138, 151, 10.1016/j.isprsjprs.2017.11.014 Mnih, 2013 Cai, 2018, Joint feature network for bridge segmentation in remote sensing images, 2515 Saito, 2015, Building and Road Detection from Large Aerial Imagery, vol. 9405, 153 Alshehhi, 2017, Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks, ISPRS J. Photogramm. Remote Sens., 130, 139, 10.1016/j.isprsjprs.2017.05.002 Maggiori, 2016, Fully convolutional neural networks for remote sensing image classification, 5071 Badrinarayanan, 2017, SegNet: a deep convolutional encoder-decoder architecture for image segmentation, IEEE Trans. Image Process., 39, 2481 Cheng, 2017, Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network, IEEE Trans. Geosci. Remote Sens., 55, 3322, 10.1109/TGRS.2017.2669341 Li, 2021, Automatic road extraction from remote sensing imagery using ensemble learning and postprocessing, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 14, 10535, 10.1109/JSTARS.2021.3094673 Behera, 2022, The NITRDrone dataset to address the challenges for road extraction from aerial images, J. Signal Process. Syst. Wu, 2012, Superpixel-based unsupervised change detection using multi-dimensional change vector analysis and SVM-based classification, 257 Audebert, 2016, How useful is region-based classification of remote sensing images in a deep learning framework?, 5091 Zhao, 2017, Superpixel-based multiple local CNN for panchromatic and multispectral image classification, IEEE Trans. Geosci. Remote Sens., 55, 4141, 10.1109/TGRS.2017.2689018 Ali, 2017, A new proposed the Internet of Things (IoT) virtualization framework based on sensor-as-a-service concept, Wirel. Pers. Commun., 97, 1419, 10.1007/s11277-017-4580-x Achanta, 2010 Achanta, 2012, SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Trans. Image Process., 34, 2274 LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791 Ronneberger, 2015, U-Net: convolutional networks for biomedical image segmentation, 234 Demir, 2018, DeepGlobe 2018: a challenge to parse the Earth through satellite images Deng, 2009, ImageNet: a large-scale hierarchical image database, 248 Hu, 2018, Squeeze-and-excitation networks, 7132 Huang, 2017, Densely connected convolutional networks, 4700 Pan, 2009, A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 22, 1345, 10.1109/TKDE.2009.191 Kingma, 2015, Adam: a method for stochastic optimization A.F. Agarap, Deep learning using rectified linear units (ReLu), preprint arXiv:180308375, 2018.