| Presentation | 2018-03-14 Hierarchical quaternion neural networks with self-organizing codebook for unsupervised PolSAR land classification Hyunsoo Kim, Akira Hirose, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We propose a self-organizing codebook-based hierarchical polarization feature vector generation to realize an unsupervised land classification with PolSAR (polarimetric synthetic aperture radar) data. PolSAR has reached the high-resolution of decimeter level. Conventional methods perform the spatial averaging in 10m to 20m square real-space area to classify observed land into categories such as farm, forest, and town. However, with this averaging, we can not expect to discover new detailed land classes by resolution improvement, since the resolution of the PolSAR data is lowered in the averaging process. Our proposal in this paper generates feature vectors useful for classifying the land pieces into categories while preserving the detailed polarization features in respective pixels of the high-resolution PolSAR data. Then, the method discovers the new detailed land classes that can be available only in the high-resolution PolSAR data. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | polarimetric synthetic aperture radar (PolSAR) / unsupervised land classification / high-resolution / hierarchical polarization feature / quaternion neural networks / Poincare parameter / auto-encoder / self-organizing map (SOM) |
| Paper # | NC2017-88 |
| Date of Issue | 2018-03-06 (NC) |
| Conference Information | |
| Committee | MBE / NC |
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| Conference Date | 2018/3/13(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kikai-Shinko-Kaikan Bldg. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | ME, general |
| Chair | Kazuki Nakajima(Univ. of Toyama) / Masafumi Hagiwara(Keio Univ.) |
| Vice Chair | Masaki Kyoso(TCU) / Yutaka Hirata(Chubu Univ.) |
| Secretary | Masaki Kyoso(Toyama Pref. Univ.) / Yutaka Hirata(Kindai Univ.) |
| Assistant | Kim Juhyon(Univ. of Toyama) / Takumi Kobayashi(YNU) / Yoshihisa Shinozawa(Keio Univ.) / Keiichiro Inagaki(Chubu Univ.) |
| Paper Information | |
| Registration To | Technical Committee on ME and Bio Cybernetics / Technical Committee on Neurocomputing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Hierarchical quaternion neural networks with self-organizing codebook for unsupervised PolSAR land classification |
| Sub Title (in English) | |
| Keyword(1) | polarimetric synthetic aperture radar (PolSAR) |
| Keyword(2) | unsupervised land classification |
| Keyword(3) | high-resolution |
| Keyword(4) | hierarchical polarization feature |
| Keyword(5) | quaternion neural networks |
| Keyword(6) | Poincare parameter |
| Keyword(7) | auto-encoder |
| Keyword(8) | self-organizing map (SOM) |
| 1st Author's Name | Hyunsoo Kim |
| 1st Author's Affiliation | The University of Tokyo(Tokyo Univ.) |
| 2nd Author's Name | Akira Hirose |
| 2nd Author's Affiliation | The University of Tokyo(Tokyo Univ.) |
| Date | 2018-03-14 |
| Paper # | NC2017-88 |
| Volume (vol) | vol.117 |
| Number (no) | NC-508 |
| Page | pp.pp.121-126(NC), |
| #Pages | 6 |
| Date of Issue | 2018-03-06 (NC) |