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| 講演名 | 2022-06-28 13:30 Joint-Conditional Mutual Information Based Feature Subset Selection for Remotely Sensed Hyperspectral Image Classification ○U A Md Ehsan Ali・Keisuke Kameyama（Univ. Tsukuba）  NC2022-16 IBISML2022-16 |
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| 抄録 | （和） | Hundreds of contiguous bands of remotely sensed hyperspectral image (HSI) capture the spectral signatures of observed objects or materials on the earth’s surface. Although the HSI data is able to provide huge information with great details, it poses challenges to image analysis because of the high computational cost due to the large dimensionality of the feature space, redundancy in information, and curse of dimensionality. To overcome these difficulties, feature reduction techniques are used to extract informative features from hyperspectral images. This paper proposes an information-theoretic feature selection approach for selecting an informative feature subset considering the maximum of the minimum approach. The minimum of conditional mutual information based estimation is used to select a feature among the selected feature subset. This selected feature with the corresponding candidate feature is then exploited using the mutual information and joint mutual information based valuation to find the maximum relevance of the candidate features with the target classes. The effectiveness of the proposed approach, called joint-conditional mutual information for selecting informative feature (JCIF), is assessed by implementing it in some synthetic data and two remotely sensed HSI data. Several known feature selection algorithms are also used for comparison purposes. The results of the experiments with K-Nearest Neighbors and Support Vector Machine classifiers reveal that JCIF performs better in selecting informative features. |
| （英） | Hundreds of contiguous bands of remotely sensed hyperspectral image (HSI) capture the spectral signatures of observed objects or materials on the earth’s surface. Although the HSI data is able to provide huge information with great details, it poses challenges to image analysis because of the high computational cost due to the large dimensionality of the feature space, redundancy in information, and curse of dimensionality. To overcome these difficulties, feature reduction techniques are used to extract informative features from hyperspectral images. This paper proposes an information-theoretic feature selection approach for selecting an informative feature subset considering the maximum of the minimum approach. The minimum of conditional mutual information based estimation is used to select a feature among the selected feature subset. This selected feature with the corresponding candidate feature is then exploited using the mutual information and joint mutual information based valuation to find the maximum relevance of the candidate features with the target classes. The effectiveness of the proposed approach, called joint-conditional mutual information for selecting informative feature (JCIF), is assessed by implementing it in some synthetic data and two remotely sensed HSI data. Several known feature selection algorithms are also used for comparison purposes. The results of the experiments with K-Nearest Neighbors and Support Vector Machine classifiers reveal that JCIF performs better in selecting informative features. |
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| キーワード | （和） | Hyperspectral imaging / Feature selection / Mutual Information / / / / / |
| （英） | Hyperspectral imaging / Feature selection / Mutual Information / / / / / | |
| 文献情報 | 信学技報, vol. 122, no. 90, IBISML2022-16, pp. 115-122, 2022年6月. | |
| 資料番号 | IBISML2022-16 | |
| 発行日 | 2022-06-20 (NC, IBISML) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | NC2022-16 IBISML2022-16 |
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| 研究会情報 | |
| 研究会 | NC IBISML IPSJ-BIO IPSJ-MPS |
| 開催期間 | 2022-06-27 - 2022-06-29 |
| 開催地（和） | 琉球大学50周年記念館 |
| 開催地（英） | |
| テーマ（和） | 機械学習によるバイオデータマイニング、一般 |
| テーマ（英） | |
| 講演論文情報の詳細 | |
| 申込み研究会 | IBISML |
| 会議コード | 2022-06-NC-IBISML-BIO-MPS |
| 本文の言語 | 英語 |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Joint-Conditional Mutual Information Based Feature Subset Selection for Remotely Sensed Hyperspectral Image Classification |
| サブタイトル（英） | |
| キーワード(1)（和/英） | Hyperspectral imaging / Hyperspectral imaging |
| キーワード(2)（和/英） | Feature selection / Feature selection |
| キーワード(3)（和/英） | Mutual Information / Mutual Information |
| キーワード(4)（和/英） | / |
| キーワード(5)（和/英） | / |
| キーワード(6)（和/英） | / |
| キーワード(7)（和/英） | / |
| キーワード(8)（和/英） | / |
| 第1著者 氏名（和/英/ヨミ） | U A Md Ehsan Ali / U A Md Ehsan Ali / |
| 第1著者 所属（和/英） | University of Tsukuba (略称： 筑波大) University of Tsukuba (略称： Univ. Tsukuba) |
| 第2著者 氏名（和/英/ヨミ） | Keisuke Kameyama / Keisuke Kameyama / |
| 第2著者 所属（和/英） | University of Tsukuba (略称： 筑波大) University of Tsukuba (略称： Univ. Tsukuba) |
| 講演者 | 第1著者 |
| 発表日時 | 2022-06-28 13:30:00 |
| 発表時間 | 25分 |
| 申込先研究会 | IBISML |
| 資料番号 | NC2022-16, IBISML2022-16 |
| 巻番号（vol） | vol.122 |
| 号番号（no） | no.89(NC), no.90(IBISML) |
| ページ範囲 | pp.115-122 |
| ページ数 | 8 |
| 発行日 | 2022-06-20 (NC, IBISML) |
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