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Research Article

Standardized object-based dual CNNs for very high-resolution remote sensing image classification and standardization combination effect analysis

ORCID Icon, , , &
Pages 6635-6663 | Received 29 Sep 2019, Accepted 26 Jan 2020, Published online: 17 Jun 2020

References

  • Achanta, R., A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk. 2010. “SLIC Superpixels.” Technical Report 149300. EPFL, June, Lausanne, Switzerland.
  • Achanta, R., A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk. 2012. “SLIC Superpixels Compared to State-of-the-Art Superpixel Methods.” IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11): 2274–2282. doi:10.1109/TPAMI.2012.120.
  • Audebert, N., B. Le Saux, and S. Lefèvre. 2017. “Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images.” Remote Sensing 9 (4): 368. doi:10.3390/rs9040368.
  • Benediktsson, J., J. Chanussot, and W. Moon. 2013. “Advances in Very-High-Resolution Remote Sensing.” Proceedings of the IEEE 101 (3): 566–569. doi:10.1109/JPROC.2012.2237076.
  • Blaschke, T., G. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R. Queiroz Feitosa, et al. 2014. “Geographic Object-Based Image Analysis – Towards a New Paradigm.” ISPRS Journal of Photogrammetry and Remote Sensing 87:180–191. doi:10.1016/j.isprsjprs.2013.09.014.
  • Cánovas-García, F., and F. Alonso-Sarría. 2015. “A Local Approach to Optimize the Scale Parameter in Multiresolution Segmentation for Multispectral Imagery.” Geocarto International 30 (8): 937–961. doi:10.1080/10106049.2015.1004131.
  • Cao, J., Z. Chen, and B. Wang. 2016. “Deep Convolutional Networks with Superpixel Segmentation for Hyperspectral Image Classification.” Paper presented at International Geoscience and Remote Sensing Symposium, Beijing, China, July 10–15.
  • Chen, Y., D. Ming, and X. Lv. 2019. “Superpixel Based Land Cover Classification of VHR Satellite Image Combining Multi-scale CNN and Scale Parameter Estimation.” Earth Science Informatics 12 (3): 341–363. doi:10.1007/s12145-019-00383-2.
  • Cheng, G., J. Han, and X. Lu. 2017. “Remote Sensing Image Scene Classification: Benchmark and State of the Art.” Proceedings of the IEEE 105 (10): 1865–1883. doi:10.1109/JPROC.2017.2675998.
  • Cireşan, D., U. Meier, and J. Schmidhuber. 2012. “Multi-column Deep Neural Networks for Image Classification.” arXiv:1202.2745 [Cs].
  • Comaniciu, D., and P. Meer. 2002. “Mean Shift: A Robust Approach Toward Feature Space Analysis.” IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (5): 603–619. doi:10.1109/34.1000236.
  • Djerriri, K., and M. S. Karoui 2017. “Classification of Quickbird Imagery over Urban Area Using Convolutional Neural Network.” Paper presented at the 2017 Joint Urban Remote Sensing Event (JURSE), March, Dubai, United Arab Emirates. doi:10.1109/JURSE.2017.7924631.
  • Girshick, R., J. Donahue, T. Darrell, and J. Malik. 2014. “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.” Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 23–28, Columbus, OH, USA. doi:10.1109/CVPR.2014.81.
  • Hershey, S., S. Chaudhuri, D. P. W. Ellis, J. F. Gemmeke, A. Jansen, R. C. Moore, M. Plakal, et al. 2017. “CNN Architectures for Large-scale Audio Classification.” Paper presented at the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 5–8,  New Orleans, LA, USA. doi:10.1109/ICASSP.2017.7952132.
  • Ioffe, S., and C. Szegedy. 2015 February. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.” ArXiv:1502.03167 [Cs]. http://arxiv.org/abs/1502.03167
  • Kavzoglu, T., M. Erdemir, and H. Tonbul. 2017. “Classification of Semiurban Landscapes from Very High-resolution Satellite Images Using a Regionalized Multiscale Segmentation Approach.” Journal of Applied Remote Sensing 11 (3): 035016. doi:10.1117/1.JRS.11.035016.
  • Lawrence, S., C. L. Giles, A. Tsoi, and A. D. Back. 1997. “Face Recognition: A Convolutional Neural-network Approach.” IEEE Transactions on Neural Networks 8 (1): 98–113. doi:10.1109/72.554195.
  • Liu, Y., D. Nguyen, N. Deligiannis, W. Ding, and A. Munteanu. 2017. “Hourglass-shapenetwork Based Semantic Segmentation for High Resolution Aerial Imagery.” Remote Sensing 9 (6): 522. doi:10.3390/rs9060522.
  • Long, Y., Y. Gong, Z. Xiao, and Q. Liu. 2017. “Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing 55 (5): 2486–2498. doi:10.1109/TGRS.2016.2645610.
  • Lv, X., D. Ming, Y. Chen, and M. Wang. 2019. “Very High Resolution Remote Sensing Image Classification with SEEDS-CNN and Scale Effect Analysis for Superpixel CNN Classification.” International Journal of Remote Sensing 40 (2): 506–531. doi:10.1080/01431161.2018.1513666.
  • Mahdianpari, M., B. Salehi, M. Rezaee, F. Mohammadimanesh, and Y. Zhang. 2018. “Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery.” Remote Sensing 10 (7): 1119. doi:10.3390/rs10071119.
  • Makantasis, K., K. Karantzalos, A. Doulamis, and N. Doulamis. 2015. “Deep Supervised Learning for Hyperspectral Data Classification through Convolutional Neural Networks.” Paper presented at the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 26–31, Milan, Italy, doi:10.1109/IGARSS.2015.7326945.
  • Mash, R., B. Borghetti, and J. Pecarina. 2016. “Improved Aircraft Recognition for Aerial Refueling through Data Augmentation in Convolutional Neural Networks.”
  • Maxwell, A., T. Warner, and F. Fang. 2018. “Implementation of Machine-learning Classification in Remote Sensing: An Applied Review.” International Journal of Remote Sensing 39 (9): 2784–2817. doi:10.1080/01431161.2018.1433343.
  • Ming, D., J. Li, J. Wang, and M. Zhang. 2015. “Scale Parameter Selection by Spatial Statistics for GeOBIA: Using Mean-Shift Based Multi-Scale Segmentation as an Example.” ISPRS Journal of Photogrammetry and Remote Sensing 106 (August): 28–41. doi:10.1016/j.isprsjprs.2015.04.010.
  • Mondini, C., I. Marchesini, M. Rossi, K. Chang, G. Pasquariello, and F. Guzzetti. 2013. “Bayesian Framework for Mapping and Classifying Shallow Landslides Exploiting Remote Sensing and Topographic Data.” Geomorphology 201: 135–147. doi:10.1016/j.geomorph.2013.06.015.
  • Nogueira, K., O. Penatti, and J. Dos Santos. 2017. “Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification.” Pattern Recognition 61: 539–556. doi:10.1016/j.patcog.2016.07.001.
  • Romero, A., C. Gatta, and G. Camps-Valls. 2016. “Unsupervised Deep Feature Extraction for Remote Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 54 (3): 1349–1362. doi:10.1109/TGRS.2015.2478379.
  • Schmidhuber, J. 2015. “Deep Learning in Neural Networks: An Overview.” Neural Networks 61: 85–117. doi:10.1016/j.neunet.2014.09.003.
  • Scott, G., M. R. England, W. A. Starms, R. Marcum, and C. Davis. 2017. “Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery.” IEEE Geoscience and Remote Sensing Letters 14 (4): 549–553. doi:10.1109/LGRS.2017.2657778.
  • Sebari, I., and D. He. 2013. “Automatic Fuzzy Object-based Analysis of VHSR Images for Urban Objects Extraction.” ISPRS Journal of Photogrammetry and Remote Sensing 79: 171–184. doi:10.1016/j.isprsjprs.2013.02.006.
  • Sheeren, D., N. Bastin, A. Ouin, S. Ladet, G. Balent, and J. Lacombe. 2009. “Discriminating Small Wooded Elements in Rural Landscape from Aerial Photography: A Hybrid Pixel/object-based Analysis Approach.” International Journal of Remote Sensing 30 (19): 4979–4990. doi:10.1080/01431160903022928.
  • Strigl, D., K. Kofler, and S. Podlipnig. 2010. “Performance and Scalability of GPU-Based Convolutional Neural Networks.” Paper presented at the 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2010), February 17–19, Pisa, Italy, doi:10.1109/PDP.2010.43.
  • Tang, J., C. Deng, G. Huang, and B. Zhao. 2015. “ompressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine.” IEEE Transactions on Geoscience and Remote Sensing 53 (3): 1174–1185. doi:10.1109/TGRS.2014.2335751.
  • Van, B., M. Boix, G. Roig, B. Capitani, and L. Gool. 2012. “SEEDS: Superpixels Extracted via Energy-Driven Sampling.” International Journal of Computer Vision 111: 298–314.
  • Wang, Q., J. Zhang, X. Hu, and Y. Wang. 2016. “Automatic Detection and Classification of Oil Tanks in Optical Satellite Images Based on Convolutional Neural Network.”
  • Xia, G., J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, and X. Lu. 2017. “AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing 55 (7): 3965–3981. doi:10.1109/TGRS.2017.2685945.
  • Xiao, Z., Y. Gong, Y. Long, D. Li, X. Wang, and H. Liu. 2017. “Airport Detection Based on a Multiscale Fusion Feature for Optical Remote Sensing Images.” IEEE Geoscience and Remote Sensing Letters 14 (9): 1469–1473. doi:10.1109/LGRS.2017.2712638.
  • Xiao, Z., Q. Liu, G. Tang, and X. Zhai. 2015. “Elliptic Fourier Transformation-based Histograms of Oriented Gradients for Rotationally Invariant Object Detection in Remote-sensing Images.” International Journal of Remote Sensing 36 (2): 618–644. doi:10.1080/01431161.2014.999881.
  • Xie, M., N. Jean, M. Burke, D. Lobell, and S. Ermon. 2016. “Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping.” arXiv:1510.00098v2 [Cs].
  • Zhang, C., and P. Atkinson. 2016. “Ovel Shape Indices for Vector Landscape Pattern Analysis.” International Journal of Geographical Information Science 30 (12): 2442–2461. doi:10.1080/13658816.2016.1179313.
  • Zhang, C., X. Pan, H. Li, A. Gardiner, I. Sargent, J. Hare, and P. Atkinson. 2018. “A Hybrid MLP-CNN Classifier for Very Fine Resolution Remotely Sensed Image Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 140: 133–144. doi:10.1016/j.isprsjprs.2017.07.014.
  • Zhang, F., B. Du, L. Zhang, and M. Xu. 2016. “Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection.” IEEE Transactions on Geoscience and Remote Sensing 54 (9): 5553–5563. doi:10.1109/TGRS.2016.2569141.
  • Zhang, P., X. Niu, Y. Dou, and F. Xia. 2017. “Airport Detection on Optical Satellite Images Using Deep Convolutional Neural Networks.” IEEE Geoscience and Remote Sensing Letters 14 (8): 1183–1187. doi:10.1109/LGRS.2017.2673118.
  • Zhao, W., and S. Du. 2016. “Learning Multiscale and Deep Representations for Classifying Remotely Sensed Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 113: 155–165. doi:10.1016/j.isprsjprs.2016.01.004.
  • Zhao, W., S. Du, and W. Emery. 2017. “Object-Based Convolutional Neural Network for High-Resolution Imagery Classification.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (7): 3386–3396. doi:10.1109/JSTARS.2017.2680324.
  • Zhao, W., L. Jiao, W. Ma, J. Zhao, J. Zhao, H. Liu, X. Cao, and S. Yang. 2017. “Superpixel-Based Multiple Local CNN for Panchromatic and Multispectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 55 (7): 4141–4156. doi:10.1109/TGRS.2017.2689018.
  • Zhou, J., J. Qin, K. Gao, and H. Leng. 2016. “SVM-based Soft Classification of Urban Tree Species Using Very High-spatial Resolution Remote-sensing Imagery.” International Journal of Remote Sensing 37 (11): 2541–2559. doi:10.1080/01431161.2016.1178867.
  • Zhou, K., D. Ming, X. Lv, J. Fang, and M. Wang. 2019. “CNN-Based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data.” Remote Sensing 11 (17): 2065. doi:10.3390/rs11172065.

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