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Articles

Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold

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Pages 169-184 | Received 31 Mar 2018, Accepted 09 Jul 2018, Published online: 06 Sep 2018

References

  • Amin, H. H., Deabes, W., & Bouazza, K. (2017). Clustering of user activities based on adaptive threshold spiking neural networks. Ninth international conference on ubiquitous and future networks, IEEE, Milan, Italy, 2017, 1–6.
  • Atkinson, P. M., & Lewis, P. (2000). Geostatistical classification for remote sensing: An introduction. Computers & Geosciences, 26(4), 361–371.
  • Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495.
  • Bischof, H., Schneider, W., & Pinz A, J. (1992). Multispectral classification of Landsat-images using neural networks. IEEE Transactions on Geoscience and Remote Sensing, 30(3), 482–490.
  • Boureau, Y. L., Ponce, J., & LeCun, Y. (2010). A theoretical analysis of feature pooling in visual recognition. Proceedings of the 27th international conference on machine learning (ICML-10), 2010, 111–118.
  • Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K, & Yuille, A.L. (2018). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.
  • Cheng, G., & Han, J. (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11–28.
  • Decatur, S. E. (1989). Application of neural networks to terrain classification. International joint conference on neural networks, IEEE, Vol. 1, 1989, 283–288.
  • DSTL Satellite Imagery Feature Detection. (2017). Retrieved from https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection
  • Forestier, G., Puissant, A., Wemmert, C., & Gançarski, P. (2012). Knowledge-based region labeling for remote sensing image interpretation. Computers, Environment and Urban Systems, 36(5), 470–480.
  • Fu, G., Liu, C., Zhou, R., Sun, T., & Zhang, Q. (2017). Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sensing, 9(6), 498.
  • Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., & Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. Transactions on Pattern Analysis and Machine Intelligence, 0, 0. https://arxiv.org/abs/1704.06857
  • Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, Columbus, OH, 2014, 580–587.
  • Han, J., Zhang, D., Cheng, G., Guo, L., & Ren, J. (2015). Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Transactions on Geoscience and Remote Sensing, 53(6), 3325–3337.
  • Hentschel, C., Wiradarma, T. P., & Sack, H. (2016). Fine tuning CNNS with scarce training data – adapting imagenet to art epoch classification. IEEE international conference on image processing, IEEE, Phoenix, AZ, 2016, 3693–3697.
  • Kaiser, P. (2016). Learning city structures from online maps. ETH Zurich, 2016.
  • Kampffmeyer, M., Salberg, A. B., & Jenssen, R. (2016). Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. 2016 IEEE conference on computer vision and pattern recognition workshops (CVPRW), IEEE, Las Vegas, NV, 2016, 680–688.
  • Kemker, R., & Kanan, C. (2017). Deep neural networks for semantic segmentation of multispectral remote sensing imagery. arXiv preprint arXiv, 1703.06452.
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. 3rd International Conference for Learning Representations, San Diego, 2015.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 1097–1105. Lake Tahoe, NV, December 3–6, 2012.
  • Liu, Y., Minh Nguyen, D., Deligiannis, N., Ding, W., & Munteanu, A. (2017). Hourglass-shapenetwork based semantic segmentation for high resolution aerial imagery. Remote Sensing, 9(6), 522.
  • Ma, A., Zhong, Y., & Zhang, L. (2015). Adaptive multiobjective memetic fuzzy clustering algorithm for remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(8), 4202–4217.
  • Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2016). Fully convolutional neural networks for remote sensing image classification. Geoscience and remote sensing symposium, IEEE, 2016, 5071–5074.
  • Maggiori, E., Tarabalka, Y., Charpiat, G., & Alliez, P. (2017). Can semantic labeling methods generalize to any city? The INRIA aerial image labeling benchmark. IEEE international symposium on geoscience and Remote sensing (IGARSS), 2017.
  • Marmanis, D., Datcu, M., Esch, T., & Stilla, U. (2016a). Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geoscience and Remote Sensing Letters, 13(1), 105–109.
  • Marmanis, D., Wegner, J. D., Galliani, S., et al. (2016b). Semantic segmentation of aerial images with an ensemble of CNNs. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 473.
  • Mnih, V. (2013). Machine learning for aerial image labeling. University of Toronto, Canada, 2013.
  • Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259.
  • Muruganandham, S. (2016). Semantic segmentation of satellite images using deep learning. Lulea University of Technology, 2016.
  • Noh, H., Hong, S., & Han, B. (2015). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE international conference on computer vision, Santiago, Chile, 2015, 1520–1528.
  • Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217–222.
  • Parvat, A., Chavan, J., Kadam, S., Dev, S., & Pathak, V. (2017). A survey of deep-learning frameworks. 2017 International conference on inventive systems and control (ICISC), IEEE, Coimbatore, India, 2017, 1–7.
  • Penatti, O. A. B., Nogueira, K., & Santos, J. A. D. (2015). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? Computer vision and pattern recognition workshops, IEEE, Boston, MA, 2015, 44–51.
  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International conference on medical image computing and computer-assisted intervention, Springer, Cham, 2015, 234–241.
  • Saito, S., Yamashita, T., & Aoki, Y. (2016). Multiple object extraction from aerial imagery with convolutional neural networks. Electronic Imaging, 60(1), 10402-1–10402-9.
  • Semantic Labeling Contest[EB/OL]. (2018). Retrieved from http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.html
  • Sharma, A., Liu, X., Yang, X., & Shi, D. (2017). A patch-based convolutional neural network for remote sensing image classification. Neural Networks, 95, 19–28.
  • Shelhamer, E., Long, J., & Darrell, T. (2017). Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640–651.
  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv Preprint ArXiv, 1409.1556.
  • Tuia, D., Moser, G., Le Saux, B., et al. (2017). IEEE GRSS data fusion contest: Open data for global multimodal land Use classification [technical committees]. IEEE Geoscience and Remote Sensing Magazine, 5(1), 70–73.
  • Wei, Y., Wang, Z., & Xu, M. (2017). Road structure refined CNN for road extraction in aerial image. IEEE Geoscience & Remote Sensing Letters, 14(5), 709–713.
  • Zhang, L., Zhang, L., & Du, B. (2016). Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2), 22–40.
  • Zhao, B., Feng, J., Wu, X., & Yan, S. (2017). A survey on deep learning-based fine-grained object classification and semantic segmentation. International Journal of Automation and Computing, 14(2), 119–135.
  • Zhong, Y., Fei, F., Liu, Y., et al. (2017). SatCNN: Satellite image dataset classification using agile convolutional neural networks. Remote Sensing Letters, 8(2), 136–145.

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