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2017 | OriginalPaper | Buchkapitel

Bidirectional Adaptive Feature Fusion for Remote Sensing Scene Classification

verfasst von : Weijun Ji, Xuelong Li, Xiaoqiang Lu

Erschienen in: Computer Vision

Verlag: Springer Singapore

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Abstract

Convolutional neural networks (CNN) have been excellent for scene classification in nature scene. However, directly using the pre-trained deep models on the aerial image is not proper, because of the spatial scale variability and rotation variability of the HSR remote sensing images. In this paper, a bidirectional adaptive feature fusion strategy is investigated to deal with the remote sensing scene classification. The deep learning feature and the SIFT feature are fused together to get a discriminative image presentation. The fused feature can not only describe the scenes effectively by employing deep learning feature but also overcome the scale and rotation variability with the usage of the SIFT feature. By fusing both SIFT feature and global CNN feature, our method achieves state-of-the-art scene classification performance on the UCM and the AID datasets.

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Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH

Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via pLSA. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 517–530. Springer, Heidelberg (2006). https://doi.org/10.1007/11744085_40 CrossRef

Castelluccio, M., Poggi, G., Sansone, C., Verdoliva, L.: Land use classification in remote sensing images by convolutional neural networks. J. Mol. Struct. Theochem 537(1), 163–172 (2015)

Cheng, G., Ma, C., Zhou, P., Yao, X., Han, J.: Scene classification of high resolution remote sensing images using convolutional neural networks. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, pp. 767–770 (2016)

Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54(12), 7405–7415 (2016)CrossRef

Cheriyadat, A.M.: Unsupervised feature learning for aerial scene classification. IEEE Trans. Geosci. Remote Sens. 52(1), 439–451 (2014)CrossRef

Chung, J., Gulcehre, C., Cho, K.H., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. Eprint Arxiv (2014)

Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649 (2013)

Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)

10.

Li, X., Lu, Q., Dong, Y., Tao, D.: SCE: a manifold regularized set-covering method for data partitioning. IEEE Trans. Neural Netw. Learn. Syst. (2017)

11.

Liu, Y., Zhong, Y., Fei, F., Zhang, L.: Scene semantic classification based on random-scale stretched convolutional neural network for high-spatial resolution remote sensing imagery. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, pp. 763–766 (2016)

12.

Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRef

13.

Lu, X., Zheng, X., Yuan, Y.: Remote sensing scene classification by unsupervised representation learning. IEEE Trans. Geosci. Remote Sens. 55, 5185–5197 (2017)CrossRef

14.

Luus, F.P.S., Salmon, B.P., Bergh, F.V.D., Maharaj, B.T.J.: Multiview deep learning for land-use classification. IEEE Geosci. Remote Sens. Lett. 12(12), 1–5 (2015)CrossRef

15.

Nogueira, K., Penatti, O.A.B., Santos, J.A.D.: Towards better exploiting convolutional neural networks for remote sensing scene classification. Patt. Recogn. 61, 539–556 (2017)CrossRef

16.

Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

17.

Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_11 CrossRef

18.

Sheng, G., Yang, W., Xu, T., Sun, H.: High-resolution satellite scene classification using a sparse coding based multiple feature combination. Int. J. Remote Sens. 33(8), 2395–2412 (2012)CrossRef

19.

Xia, G.S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., Lu, X.: AID: a benchmark data set for performance evaluation of aerial scene classification. IEEE Trans. Geosci. Remote Sens. 55, 3965–3981 (2017)CrossRef

20.

Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of Sigspatial International Conference on Advances in Geographic Information Systems, pp. 270–279 (2010)

21.

Yang, Y., Newsam, S.: Spatial pyramid co-occurrence for image classification. In: Proceedings of International Conference on Computer Vision, pp. 1465–1472 (2011)

22.

Zhang, F., Du, B., Zhang, L.: Saliency-guided unsupervised feature learning for scene classification. IEEE Trans. Geosci. Remote Sens. 53(4), 2175–2184 (2015)CrossRef

23.

Zhang, F., Du, B., Zhang, L.: Scene classification via a gradient boosting random convolutional network framework. IEEE Trans. Geosci. Remote Sens. 54(3), 1793–1802 (2016)CrossRef

24.

Zheng, X., Sun, X., Fu, K., Wang, H.: Automatic annotation of satellite images via multifeature joint sparse coding with spatial relation constraint. IEEE Geosci. Remote Sens. Lett. 10(4), 652–656 (2012)CrossRef

25.

Zhong, Y., Zhu, Q., Zhang, L.: Scene classification based on multifeature probabilistic latent semantic analysis for high spatial resolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 53(11), 6207–6222 (2015)CrossRef

26.

Zou, Q., Ni, L., Zhang, T., Wang, Q.: Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 12(11), 2321–2325 (2015)CrossRef

Titel: Bidirectional Adaptive Feature Fusion for Remote Sensing Scene Classification
verfasst von: Weijun Ji
Xuelong Li
Xiaoqiang Lu
Verlag: Springer Singapore
Buch: Computer Vision
Print ISBN: 978-981-10-7301-4

Electronic ISBN: 978-981-10-7302-1

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-981-10-7302-1_40

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