RS-DCNN: A novel distributed convolutional-neural-networks based-approach for big remote-sensing image classification
Tài liệu tham khảo
Acar, 2020, Detection of unregistered electric distribution transformers in agricultural fields with the aid of Sentinel-1 SAR images by machine learning approaches, Comput. Electron. Agric., 175, 10.1016/j.compag.2020.105559
Al-Sarem, 2019, Deep learning-based rumor detection on microblogging platforms: A systematic review, IEEE Access, 7, 152788, 10.1109/ACCESS.2019.2947855
Ben Atitallah, 2020, Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions, Comput. Sci. Rev., 38
Borja, 2019, End-to-end sequence labeling via deep learning for automatic extraction of agricultural regulations, Comput. Electron. Agric., 162, 106, 10.1016/j.compag.2019.03.027
Boulila, 2019, A top-down approach for semantic segmentation of big remote sensing images, Earth Sci. Inf., 12, 295, 10.1007/s12145-018-00376-7
Boulila, 2018, A Novel Decision Support System for the Interpretation of Remote Sensing Big Data, J. Earth Sci. Inform., 11, 31, 10.1007/s12145-017-0313-7
Boulila, 2011, A data mining based approach to predict spatiotemporal changes in satellite images, Int. J. Appl. Earth Obs. Geoinf., 13, 386
Breiman, 2001, Random Forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324
Cavallaro, 2015, On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 4634, 10.1109/JSTARS.2015.2458855
Chebbi, 2018, A comparison of big remote sensing data processing with Hadoop MapReduce and Spark, 1
I. Chebbi, W. Boulila, I. R. Farah, Improvement of satellite image classification: Approach based on Hadoop/MapReduce, 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Monastir, 2016, pp. 31-34.
I. Chebbi, W. Boulila,I. R. Farah, Big data: Concepts, challenges and applications, Computational collective intelligence, Springer, Cham, pp. 638-647, 2015.
J. Chen, X. Pan, R. Monga, S. Bengio, R. Jozefowicz, Revisiting distributed synchronous SGD, arXiv preprint arXiv:1604.00981, 2016.
Chen, 2020, Augmenting a deep-learning algorithm with canal inspection knowledge for reliable water leak detection from multispectral satellite images, Adv. Eng. Inf., 46, 10.1016/j.aei.2020.101161
D. Ciresan, A. Giusti, A.L. M. Gambardella, J. Schmidhuber, Deep neural networks segment neuronal membranes in electron microscopy images. In Proceedings of the Neural Information Processing Systems 2012, Lake Tahoe, NV, USA, 3 December 2012; pp. 2843–2851.
Corbane, 2017, Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping, Big Earth Data, 1, 118, 10.1080/20964471.2017.1397899
Del Frate, 2007, Use of Neural Networks for Automatic Classification From High-Resolution Images, IEEE Trans. Geosci. Remote Sens., 45, 800, 10.1109/TGRS.2007.892009
Dong, 2016, A Hierarchical Distributed Processing Framework for Big Image Data, IEEE Trans. Big Data, 2, 297, 10.1109/TBDATA.2016.2613992
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, Proceedings of the Advances in neural information processing systems, pp. 2672–2680, 2014.
Hajjaji, 2021, Big data and IoT-based applications in smart environments: A systematic review, Comput. Sci. Rev., 39, 10.1016/j.cosrev.2020.100318
Ieracitano, 2019, A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings, Neurocomputing, 323, 96, 10.1016/j.neucom.2018.09.071
Jin, 2017, Deep convolutional neural network for inverse problems in imaging, IEEE Trans. Image Process., 26, 4509, 10.1109/TIP.2017.2713099
X. Lian, C. Zhang, H. Zhang, C. J. Hsieh, W. Zhang, J. Liu, Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent, Adv. Neural Inform. Process. Syst., pp. 5330-5340, 2017.
Liu, 2019, A Spark-Based Parallel Fuzzy c-Means Segmentation Algorithm for Agricultural Image Big Data, IEEE Access, 7, 42169, 10.1109/ACCESS.2019.2907573
Ma, 2015, Remote sensing big data computing: Challenges and opportunities, Future Generat. Comput. Syst., 51, 47, 10.1016/j.future.2014.10.029
Ma, 2019, Deep learning in remote sensing applications: A meta-analysis and review, ISPRS J. Photogramm. Remote Sens., 152, 166, 10.1016/j.isprsjprs.2019.04.015
E. Maggiori, Y. Tarabalka, G. Charpiat, P. Alliez, Fully Convolutional Neural Networks for Remote Sensing Image Classification. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10–15 July 2016; pp. 5071–5074.
Maggiori, 2017, Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification, IEEE Trans. Geosci. Remote Sens., 55, 645, 10.1109/TGRS.2016.2612821
Marmanis, 2018, Classification with an edge: Improving semantic image segmentation with boundary detection, ISPRS J. Photogramm. Remote Sens., 135, 158, 10.1016/j.isprsjprs.2017.11.009
V. Mnih, J. M. Susskind, G. E. Hinton, Modeling natural images using gated MRFs, IEEE Trans. Pattern Analy. Mach. Intell., vol. 35, no. 9, pp.2206-2222, 2013.
A. Plaza, J. A. Benediktsson, J.W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, G. Trianni, Recent advances in techniques for hyperspectral image processing, Rem. Sens. Environ., vol. 113, no. 1, pp. S110-S122, 2009.
M. C. A. Picoli, G. Camara, I. Sanches, R. Simões, A. Carvalho, A. Maciel, A. Coutinho, J. Esquerdo, J. Antunes, R. A. Begotti, D. Arvor, C. Almeida, Big earth observation time series analysis for monitoring Brazilian agriculture, ISPRS J. Photogram. Rem. Sens., vol. 145, Part B, pp. 328-339, 2018.
Rathore, 2015, Real-Time Big Data Analytical Architecture for Remote Sensing Application, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 4610, 10.1109/JSTARS.2015.2424683
Congalton, 1991, A review of assessing the accuracy of classifications of remotely sensed data, Remote Sens. Environ., 37, 35, 10.1016/0034-4257(91)90048-B
Soille, 2018, A versatile data-intensive computing platform for information retrieval from big geospatial data, Future Generat. Comput. Syst., 81, 30, 10.1016/j.future.2017.11.007
Srivastava, 2011, Distributed asynchronous constrained stochastic optimization, IEEE J. Sel. Top. Signal Process., 5, 772, 10.1109/JSTSP.2011.2118740
Talia, 2013, Clouds for Scalable Big Data Analytics, Computer, 46, 98, 10.1109/MC.2013.162
Wu, 2020, Extracting apple tree crown information from remote imagery using deep learning, Comput. Electron. Agric., 174, 10.1016/j.compag.2020.105504
Xing, 2019, Intelligent Classification Method of Remote Sensing Image Based on Big Data in Spark, Environment, 26, 183
Yin, 2018, Large Scale Remote Sensing Image Segmentation Based on Fuzzy Region Competition and Gaussian Mixture Model, IEEE Access, 6, 26069, 10.1109/ACCESS.2018.2834960
Zhang, 2016, Deep learning for remote sensing data: A technical tutorial on the state of the art, IEEE Geosci. Remote Sens. Mag., 4, 22, 10.1109/MGRS.2016.2540798
Zhang, 2018, Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery, Sensors, 18, 3717, 10.3390/s18113717
Zhao, 2016, Learning multiscale and deep representations for classifying remotely sensed imagery, ISPRS J. Photogramm. Remote Sens., 113, 155, 10.1016/j.isprsjprs.2016.01.004
Zhu, 2017, Deep learning in remote sensing: a comprehensive review and list of resources, IEEE Geosci. Remote Sens. Mag., 5, 8, 10.1109/MGRS.2017.2762307
