| 講演名 | 2015-11-24 Adaptive Nearest Feature Space Method for Remote Sensing Images Classification Yang-Lang Chang(NTUT), Chihyuan Chu(G-AVE), Hirokazu Kobayashi(OIT), Tzu-Wei Tseng(NTUT), |
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| 抄録(和) | In this paper a novel technique based on nearest feature space (NFS), known as adaptive nearest feature space (ANFS), is proposed for supervised remote sensing image classification. The NFS has been proven to be efficient for remote sensing image classification in recent years. Although NFS can perform well for classification, in some instances, it decreases the efficiency when samples of different classes are not far apart or even overlapped. Due to the different neighborhood structures of overlapping training labels, the traditional NFS can't perform well for classification of remote sensing images. In response, ANFS is proposed to overcome this problem. It combines and adopts two methods, NFS and incenter-based nearest feature space (INFS) which makes use of the incircle of three labeled samples to form an INFS, to achieve the best classification accuracy. In ANFS, a fitting preprocessing of NFS is presented to determine what the best-fix model (NFS/INFS) is for the three nearest labeled samples of the same classes. Experimental results demonstrate the proposed ANFS approach is suitable for land cover classification in earth remote sensing. It can achieve the better performance than NFS classifier when the class samples distribution overlaps. |
| 抄録(英) | In this paper a novel technique based on nearest feature space (NFS), known as adaptive nearest feature space (ANFS), is proposed for supervised remote sensing image classification. The NFS has been proven to be efficient for remote sensing image classification in recent years. Although NFS can perform well for classification, in some instances, it decreases the efficiency when samples of different classes are not far apart or even overlapped. Due to the different neighborhood structures of overlapping training labels, the traditional NFS can't perform well for classification of remote sensing images. In response, ANFS is proposed to overcome this problem. It combines and adopts two methods, NFS and incenter-based nearest feature space (INFS) which makes use of the incircle of three labeled samples to form an INFS, to achieve the best classification accuracy. In ANFS, a fitting preprocessing of NFS is presented to determine what the best-fix model (NFS/INFS) is for the three nearest labeled samples of the same classes. Experimental results demonstrate the proposed ANFS approach is suitable for land cover classification in earth remote sensing. It can achieve the better performance than NFS classifier when the class samples distribution overlaps. |
| キーワード(和) | remote sensing images classification / nearest feature space / incenter-based nearest feature space / adaptive nearest feature space |
| キーワード(英) | remote sensing images classification / nearest feature space / incenter-based nearest feature space / adaptive nearest feature space |
| 資料番号 | SANE2015-62 |
| 発行日 | 2015-11-16 (SANE) |
| 研究会情報 | |
| 研究会 | SANE |
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| 開催期間 | 2015/11/23(から2日開催) |
| 開催地（和） | アジア工科大学，バンコク，タイ |
| 開催地（英） | AIT, Bangkok, Thailand |
| テーマ（和） | ICSANE 2015 |
| テーマ（英） | ICSANE 2015 |
| 委員長氏名（和） | 小林 弘一(阪工大) |
| 委員長氏名（英） | Hirokazu Kobayashi(Osaka Inst. of Tech.) |
| 副委員長氏名（和） | 辻 政信(JAXA) / 水野 貴秀(JAXA) |
| 副委員長氏名（英） | Masanobu Tsuji(JAXA) / Takahide Mizuno(JAXA) |
| 幹事氏名（和） | 木寺 正平(電通大) / 牧 謙一郎(JAXA) |
| 幹事氏名（英） | Shohei Kidera(Univ. of Electro-Comm.) / Kenichiro Maki(JAXA) |
| 幹事補佐氏名（和） | 小幡 康(三菱電機) / 毛塚 敦(電子航法研) |
| 幹事補佐氏名（英） | Yasushi Obata(Mitsubishi Electric) / Atsushi Kezuka(ENRI) |
| 講演論文情報詳細 | |
| 申込み研究会 | Technical Committee on Space, Aeronautical and Navigational Electronics |
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| 本文の言語 | ENG |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Adaptive Nearest Feature Space Method for Remote Sensing Images Classification |
| サブタイトル（和） | |
| キーワード(1)（和/英） | remote sensing images classification / remote sensing images classification |
| キーワード(2)（和/英） | nearest feature space / nearest feature space |
| キーワード(3)（和/英） | incenter-based nearest feature space / incenter-based nearest feature space |
| キーワード(4)（和/英） | adaptive nearest feature space / adaptive nearest feature space |
| 第 1 著者 氏名（和/英） | Yang-Lang Chang / Yang-Lang Chang |
| 第 1 著者 所属（和/英） | National Taipei University of Technology(略称：NTUT) National Taipei University of Technology(略称：NTUT) |
| 第 2 著者 氏名（和/英） | Chihyuan Chu / Chihyuan Chu |
| 第 2 著者 所属（和/英） | G-AVE Technology Company(略称：G-AVE) G-AVE Technology Company(略称：G-AVE) |
| 第 3 著者 氏名（和/英） | Hirokazu Kobayashi / Hirokazu Kobayashi |
| 第 3 著者 所属（和/英） | Osaka Institute of Technology(略称：OIT) Osaka Institute of Technology(略称：OIT) |
| 第 4 著者 氏名（和/英） | Tzu-Wei Tseng / Tzu-Wei Tseng |
| 第 4 著者 所属（和/英） | National Taipei University of Technology(略称：NTUT) National Taipei University of Technology(略称：NTUT) |
| 発表年月日 | 2015-11-24 |
| 資料番号 | SANE2015-62 |
| 巻番号（vol） | vol.115 |
| 号番号（no） | SANE-320 |
| ページ範囲 | pp.71-74(SANE), |
| ページ数 | 4 |
| 発行日 | 2015-11-16 (SANE) |