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2018 | OriginalPaper | Buchkapitel

Retrieval Across Optical and SAR Images with Deep Neural Network

verfasst von : Yifan Zhang, Wengang Zhou, Houqiang Li

Erschienen in: Advances in Multimedia Information Processing – PCM 2018

Verlag: Springer International Publishing

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Abstract

In this paper, we are dedicated to the cross-modal image retrieval between optical images and synthetic aperture radar (SAR) images. This cross-modal retrieval is a challenging task due to the different imaging mechanisms and huge heterogeneity gap. Here, we design a two-stream fully convolutional network to tackle this issue. The network maps the optical and SAR images to a common feature space for comparison. For different modal images, the comparable features are obtained by feeding them into the corresponding branch. Each branch fuses two types of features in a weighted manner. These two kinds of features root in the pooling features of VGG16 at different depths, but are refined by the well-designed channels-aggregated convolution (CAC) operation as well as semi-average pooling (SAP) operation. In order to get a better model, an extensible training approach is proposed. The training of the model is from the local to the whole. Besides, we collect an optical/SAR image retrieval (OSR) dataset. Comprehensive experiments on this dataset demonstrate the effectiveness of our proposed method.

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Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: Netvlad: CNN architecture for weakly supervised place recognition. In: CVPR, pp. 5297–5307 (2016)

Babenko, A., Lempitsky, V.: Aggregating local deep features for image retrieval. In: ICCV, pp. 1269–1277 (2015)

Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. TPAMI 39(12), 2481–2495 (2017)CrossRef

Bell, S., Lawrence Zitnick, C., Bala, K., Girshick, R.: Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. In: CVPR, pp. 2874–2883 (2016)

Bottou, L.: Stochastic gradient descent tricks. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 421–436. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_25CrossRef

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

Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: CVPR. vol. 1, pp. 539–546. IEEE (2005)

Eysenck, M.W., Keane, M.T.: Cognitive psychology: A student’s handbook. Psychology press, New York (2013)CrossRef

Fukui, K., Okuno, A., Shimodaira, H.: Image and tag retrieval by leveraging image-group links with multi-domain graph embedding. In: ICIP, pp. 221–225. IEEE (2016)

10.

He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: ICCV, pp. 1026–1034 (2015)

11.

Hong, R., Hu, Z., Wang, R., Wang, M., Tao, D.: Multi-view object retrieval via multi-scale topic models. TIP 25(12), 5814–5827 (2016)MathSciNet

12.

Hong, R., Zhang, L., Tao, D.: Unified photo enhancement by discovering aesthetic communities from flickr. TIP 25(3), 1124–1135 (2016)MathSciNet

13.

Hong, R., Zhang, L., Zhang, C., Zimmermann, R.: Flickr circles: aesthetic tendency discovery by multi-view regularized topic modeling. TMM 18(8), 1555–1567 (2016)

14.

Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM MM, pp. 675–678. ACM (2014)

15.

Li, Z., Li, Y., Gao, Y., Liu, Y.: Fast cross-scenario clothing retrieval based on indexing deep features. In: Chen, E., Gong, Y., Tie, Y. (eds.) PCM 2016. LNCS, vol. 9916, pp. 107–118. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48890-5_11CrossRef

16.

Luo, M., Chang, X., Li, Z., Nie, L., Hauptmann, A.G., Zheng, Q.: Simple to complex cross-modal learning to rank. CVIU 163, 67–77 (2017)

17.

Mnih, V.: Machine learning for aerial image labeling. Ph.D. thesis, University of Toronto (Canada) (2013)

18.

Mou, L., Schmitt, M., Wang, Y., Zhu, X.X.: A CNN for the identification of corresponding patches in SAR and optical imagery of urban scenes. In: JURSE, pp. 1–4. IEEE (2017)

19.

Qi, Y., Song, Y.Z., Zhang, H., Liu, J.: Sketch-based image retrieval via siamese convolutional neural network. In: ICIP, pp. 2460–2464. IEEE (2016)

20.

Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533 (1986)CrossRef

21.

Sharma, A., Jacobs, D.W.: Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch. In: CVPR, pp. 593–600. IEEE (2011)

22.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556

23.

Tambo, A.L., Bhanu, B.: Dynamic bi-modal fusion of images for the segmentation of pollen tubes in video. In: ICIP, pp. 148–152. IEEE (2015)

24.

Wang, K., He, R., Wang, L., Wang, W., Tan, T.: Joint feature selection and subspace learning for cross-modal retrieval. TPAMI 38(10), 2010–2023 (2016)CrossRef

25.

Wang, K., Yin, Q., Wang, W., Wu, S., Wang, L.: A comprehensive survey on cross-modal retrieval (2016). arXiv preprint arXiv:1607.06215

26.

Wang, Y., Zhu, X.X., Zeisl, B., Pollefeys, M.: Fusing meter-resolution 4-d insar point clouds and optical images for semantic urban infrastructure monitoring. TGARS 55(1), 14–26 (2017)

27.

Wegner, J.D., Ziehn, J.R., Soergel, U.: Combining high-resolution optical and insar features for height estimation of buildings with flat roofs. TGARS 52(9), 5840–5854 (2014)

28.

Zhai, X., Peng, Y., Xiao, J.: Cross-modality correlation propagation for cross-media retrieval. In: ICASSP, pp. 2337–2340. IEEE (2012)

29.

Zhong, P., Gong, Z., Li, S., Schönlieb, C.B.: Learning to diversify deep belief networks for hyperspectral image classification. TGARS 55(6), 3516–3530 (2017)

Titel: Retrieval Across Optical and SAR Images with Deep Neural Network
verfasst von: Yifan Zhang
Wengang Zhou
Houqiang Li
Verlag: Springer International Publishing
Buch: Advances in Multimedia Information Processing – PCM 2018
Print ISBN: 978-3-030-00775-1

Electronic ISBN: 978-3-030-00776-8

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00776-8_36

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