Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification
Spectral image classification uses the huge amount of information provided by spectral images to identify objects in the scene of interest. In this sense, spectral images typically contain redundant information that is removed in later processing stages. To overcome this drawback, compressive spectr...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9534664/ |
| _version_ | 1819098876401942528 |
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| author | Nelson Diaz Juan Ramirez Esteban Vera Henry Arguello |
| author_facet | Nelson Diaz Juan Ramirez Esteban Vera Henry Arguello |
| author_sort | Nelson Diaz |
| collection | DOAJ |
| description | Spectral image classification uses the huge amount of information provided by spectral images to identify objects in the scene of interest. In this sense, spectral images typically contain redundant information that is removed in later processing stages. To overcome this drawback, compressive spectral imaging (CSI) has emerged as an alternative acquisition approach that captures the relevant information using a reduced number of measurements. Various methods that classify spectral images from compressive projections have been recently reported whose measurements are captured by nonadaptive, or adaptive schemes discarding any contextual information that may help to reduce the number of captured projections. In this article, an adaptive compressive acquisition method for spectral image classification is proposed. In particular, we adaptively design coded aperture patterns for a dual-arm CSI acquisition architecture, where the first system obtains compressive multispectral projections and the second arm registers compressive hyperspectral snapshots. The proposed approach exploits the spatial contextual information captured by the multispectral arm to design the coding patterns such that subsequent snapshots acquire the scene's complementary information improving the classification performance. Results of extensive simulations are shown for two state-of-the-art databases: Pavia University and Indian Pines. Furthermore, an experimental setup that performs the adaptive sensing was built to test the performance of the proposed approach on a real dataset. The proposed approach exhibits superior performance with respect to other methods that classify spectral images from compressive measurements. |
| first_indexed | 2024-12-22T00:37:57Z |
| format | Article |
| id | doaj.art-da676e1cb0464c26a24af3d6b2740ed5 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-22T00:37:57Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-da676e1cb0464c26a24af3d6b2740ed52022-12-21T18:44:46ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01149254926610.1109/JSTARS.2021.31115089534664Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image ClassificationNelson Diaz0https://orcid.org/0000-0003-3931-0199Juan Ramirez1https://orcid.org/0000-0003-0000-1073Esteban Vera2https://orcid.org/0000-0001-8387-8131Henry Arguello3https://orcid.org/0000-0002-2202-253XDepartment of Electrical Engineering, Universidad Industrial de Santander, Bucaramanga, ColombiaDepartment of Computer Science, Universidad Rey Juan Carlos, Móstoles, SpainSchool of Electrical Engineering, Pontificia Universidad Católica de Valparaíso, Valparaiso, ChileDepartment of Computer Science, Universidad Industrial de Santander, Bucaramanga, ColombiaSpectral image classification uses the huge amount of information provided by spectral images to identify objects in the scene of interest. In this sense, spectral images typically contain redundant information that is removed in later processing stages. To overcome this drawback, compressive spectral imaging (CSI) has emerged as an alternative acquisition approach that captures the relevant information using a reduced number of measurements. Various methods that classify spectral images from compressive projections have been recently reported whose measurements are captured by nonadaptive, or adaptive schemes discarding any contextual information that may help to reduce the number of captured projections. In this article, an adaptive compressive acquisition method for spectral image classification is proposed. In particular, we adaptively design coded aperture patterns for a dual-arm CSI acquisition architecture, where the first system obtains compressive multispectral projections and the second arm registers compressive hyperspectral snapshots. The proposed approach exploits the spatial contextual information captured by the multispectral arm to design the coding patterns such that subsequent snapshots acquire the scene's complementary information improving the classification performance. Results of extensive simulations are shown for two state-of-the-art databases: Pavia University and Indian Pines. Furthermore, an experimental setup that performs the adaptive sensing was built to test the performance of the proposed approach on a real dataset. The proposed approach exhibits superior performance with respect to other methods that classify spectral images from compressive measurements.https://ieeexplore.ieee.org/document/9534664/Adaptive acquisitioncompressive spectral imagingspatial contextual informationspectral image classification |
| spellingShingle | Nelson Diaz Juan Ramirez Esteban Vera Henry Arguello Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Adaptive acquisition compressive spectral imaging spatial contextual information spectral image classification |
| title | Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification |
| title_full | Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification |
| title_fullStr | Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification |
| title_full_unstemmed | Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification |
| title_short | Adaptive Multisensor Acquisition via Spatial Contextual Information for Compressive Spectral Image Classification |
| title_sort | adaptive multisensor acquisition via spatial contextual information for compressive spectral image classification |
| topic | Adaptive acquisition compressive spectral imaging spatial contextual information spectral image classification |
| url | https://ieeexplore.ieee.org/document/9534664/ |
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