Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all ar...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/21/10502 |
| _version_ | 1827600586454859776 |
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| author | Ling Dai Guangyun Zhang Jinqi Gong Rongting Zhang |
| author_facet | Ling Dai Guangyun Zhang Jinqi Gong Rongting Zhang |
| author_sort | Ling Dai |
| collection | DOAJ |
| description | In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data. |
| first_indexed | 2024-03-09T04:38:18Z |
| format | Article |
| id | doaj.art-a5ce9725cad344579a28a8de22c05398 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:38:18Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a5ce9725cad344579a28a8de22c053982023-12-03T13:24:37ZengMDPI AGApplied Sciences2076-34172021-11-0111211050210.3390/app112110502Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed DataLing Dai0Guangyun Zhang1Jinqi Gong2Rongting Zhang3School of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, ChinaSchool of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, ChinaSchool of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, ChinaSchool of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, ChinaIn the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.https://www.mdpi.com/2076-3417/11/21/10502hyperspectral imagefeature extractioninteractive featuressparse multiclass logistic regressionautonomous learning |
| spellingShingle | Ling Dai Guangyun Zhang Jinqi Gong Rongting Zhang Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data Applied Sciences hyperspectral image feature extraction interactive features sparse multiclass logistic regression autonomous learning |
| title | Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data |
| title_full | Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data |
| title_fullStr | Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data |
| title_full_unstemmed | Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data |
| title_short | Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data |
| title_sort | autonomous learning interactive features for hyperspectral remotely sensed data |
| topic | hyperspectral image feature extraction interactive features sparse multiclass logistic regression autonomous learning |
| url | https://www.mdpi.com/2076-3417/11/21/10502 |
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