Abstract
We propose a complex-valued convolutional neural network to extract the areas having land shapes similar to samples in interferometric synthetic aperture radar (InSAR). InSAR extends its application to various earth observations such as volcano monitoring and earthquake damage estimation. Since the amount of data is increasing drastically in these years, it is necessary to structurize them in a big data framework. In this paper, experiments demonstrate that similar small volcanoes are grouped into a single class. We find that the neural network is capable of discovering unidentified lands similar to prepared samples successfully.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
G-portal. https://www.gportal.jaxa.jp/gp/top.html
De, S., Bruzzone, L., Bhattacharya, A., Bovolo, F., Chaudhuri, S.: A novel technique based on deep learning and a synthetic target datadata for classification of urban areas in PolSAR data. IEEE J. Sel. Top. Appl. Earth Obs. 11(1), 154–170 (2018)
De, S., Ratha, D., Dikshya, R., Bhattacharya, A., Chaudhuri, S.: Tensorization of multifrequency PolSAR data for classification using an autoencoder network. IEEE Geosci. Remote. Sens. Lett. 15(4), 542–546 (2018)
Espinoza-Molina, D., Datcu, M.: Earth-observation image retrieval based on content, semantics, and metadata. IEEE Trans. Geosci. Remote. Sens. 51(11), 5145–5159 (2013)
Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)
Hirose, A.: Continuous complex-valued back-propagation learning. Electron. Lett. 28(20), 1854–1855 (1992)
Hirose, A.: Applications of complex-valued neural networks to coherent optical computing using phase-sensitive detection scheme. Inf. Sci.-Appl. 2, 103–117 (1994)
Hirose, A.: Complex-Valued Neural Networks, 2nd edn. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27632-3
Hirose, A., Eckmiller, R.: Behavior control of coherent-type neural networks by carrier-frequency modulation. IEEE Trans. Neural Netw. 7(4), 1032–1034 (1996)
Hirose, A., Tsuda, S., Natsuaki, R.: Structurization of synthetic aperture radar information by using neural networks. In: SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA) 2017, pp. 1–4, November 2017. https://doi.org/10.1109/BIGSARDATA.2017.8124936
Hirose, A., Yoshida, S.: Generalization characteristics of complex-valued feedforward neural networks in relation to signal coherence. IEEE Trans. Neural Netw. Learn. Syst. 23, 541–551 (2012)
Kim, H., Hirose, A.: Codebook-based hierarchical polarization feature for unsupervised fine land classification using high-resolution PolSAR data. In: International Geoscience and Remote Sensing Symposium (IGARSS) 2018 Valencia. IEEE (to be presented 2018)
Kim, H., Hirose, A.: Polarization feature extraction using quaternion neural networks for flexible unsupervised PolSAR land classification. In: International Geoscience and Remote Sensing Symposium (IGARSS) 2018 Valencia, to be presented, July 2018
Kim, H., Hirose, A.: Unsupervised fine land classification using quaternion auto-encoder-based polarization feature extraction and self-organizing mapping. IEEE Trans. Geosci. Remote. Sens. 56(3), 1839–1851 (2018)
Marmanis, D., Datcu, M., Esch, T., Stilla, U.: Deep learning earth observation classification using imagenet pretrained networks. IEEE Geosci. Remote. Sens. Lett. 13(1), 105–109 (2016)
Oyama, K., Hirose, A.: Adaptive phase-singular-unit restoration with entire-spectrum-processing complex-valued neural networks in interferometric SAR. Electron. Lett. 54(1), 43–45 (2018)
Shang, F., Hirose, A.: Quaternion neural-network-based PolSAR land classification in Poincare-sphere-parameter space. IEEE Trans. Geosci. Remote. Sens. 52(9), 5693–5703 (2014)
Shang, F., Hirose, A.: Averaged-stokes-vector-based polarimetric SAR data interpretation. IEEE Trans. Geosci. Remote. Sens. 53(8), 4536–4547 (2015)
Suksmono, A.B., Hirose, A.: Adaptive complex-amplitude texture classifier that deals with both height and reflectance for interferometric SAR images. IEICE Trans. Electron. E83–C(12), 1912–1916 (2000)
Tănase, R., Bahmanyar, R., Schwarz, G., Datcu, M.: Discovery of semantic relationships in polsar images using latent Dirichlet allocation. IEEE Geosci. Remote. Sens. Lett. 14(2), 237–241 (2017)
Acknowledgment
A part of this work was supported by JSPS KAKENHI Grant Numbers 15H02756 and 18H04105, and also by the Cooperative Research Project Program of the Research Institute of Electrical Communication (RIEC), Tohoku University. The Advanced Land Observing Satellite (ALOS) original data are copyrighted by Japan Aerospace Exploration Agency (JAXA) and provided under JAXA Fourth ALOS Research Announcement PI No. 1154.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Sunaga, Y., Natsuaki, R., Hirose, A. (2018). Proposal of Complex-Valued Convolutional Neural Networks for Similar Land-Shape Discovery in Interferometric Synthetic Aperture Radar. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-04167-0_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04166-3
Online ISBN: 978-3-030-04167-0
eBook Packages: Computer ScienceComputer Science (R0)