Abstract
Scene interpretation of remote sensing images entails effective spatial feature information extraction and application of an appropriate pattern recognition algorithm for feature learning. In literature, state-of-the-art results are attained in remote sensing using pre-trained convolutional neural networks (convNets or CNNs) for transfer learning in deep feature extraction and then applying classifiers to learn the features for scene classification. This work proposes a method that utilizes VGG-16 model for feature extraction and the multi-grain forest for feature learning and classification with ensemble classifiers majority voting. The Effectiveness of the proposed method is evaluated with ucmerced and WHU-Siri public datasets. Improved classification results are attained with the proposed method as compared to methods in the literature.
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Nogueira, K., Penatti, O.A., Dos Santos, J.A.: Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recogn. 61, 539–556 (2017)
Hu, F., Xia, G.S., Hu, J., Zhang, L.: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7(11), 14680–14707 (2015)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Zhou, Z.H., Feng, J.: Deep forest. arXiv preprint arXiv:1702.08835 (2017)
Jiang, S., Zhao, H., Wu, W., Tan, Q.: A novel framework for remote sensing image scene classification. Int. Arch. Photogram. Remote Sens. Spatial Inf. Sci. 42(3), 657–663 (2018)
Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. CRC Press, Boca Raton (2012)
Cheng, G., Li, Z., Yao, X., Guo, L., Wei, Z.: Remote sensing image scene classification using bag of convolutional features. IEEE Geosci. Remote Sens. Lett. 14(10), 1735–1739 (2017)
Liu, Q., Hang, R., Song, H., Zhu, F., Plaza, J., Plaza, A.: Adaptive deep pyramid matching for remote sensing scene classification. arXiv preprint arXiv:1611.03589 (2016)
Gong, X., Xie, Z., Liu, Y., Shi, X., Zheng, Z.: Deep salient feature based anti-noise transfer network for scene classification of remote sensing imagery. Remote Sens. 10(3), 410 (2018)
Chaib, S., Liu, H., Gu, Y., Yao, H.: Deep feature fusion for VHR remote sensing scene classification. IEEE Trans. Geosci. Remote Sens. 55(8), 4775–4784 (2017)
Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, August 2016
Zhou, W., Newsam, S., Li, C., Shao, Z.: Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval. Remote Sens. 9(5), 489 (2017)
Bazi, Y., Al Rahhal, M.M., Alhichri, H., Alajlan, N.: Simple yet effective fine-tuning of deep CNNs using an auxiliary classification loss for remote sensing scene classification. Remote Sens. 11(24), 2908 (2019)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 270–279, November 2010
Xia, G.S., Yang, W., Delon, J., Gousseau, Y., Sun, H., Maître, H.: Structural high-resolution satellite image indexing, July 2010
Xia, G.S., et al.: AID: A benchmark data set for performance evaluation of aerial scene classification. IEEE Trans. Geosci. Remote Sens. 55(7), 3965–3981 (2017)
Cheng, G., Han, J., Lu, X.: Remote sensing image scene classification: benchmark and state of the art. Proc. IEEE 105(10), 1865–1883 (2017)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/a:1010933404324
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Tombe, R., Viriri, S., Dombeu, J.V.F. (2020). Scene Classification of Remote Sensing Images Based on ConvNet Features and Multi-grained Forest. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12509. Springer, Cham. https://doi.org/10.1007/978-3-030-64556-4_57
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DOI: https://doi.org/10.1007/978-3-030-64556-4_57
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