Task-Driven Onboard Real-Time Panchromatic Multispectral Fusion Processing Approach for High-Resolution Optical Remote Sensing Satellite
Onboard real-time processing of remote sensing satellites is an important means of rapidly obtaining information, and the fusion processing of panchromatic and multispectral data is of great significance for optical satellites. In order to ensure the effect, most traditional algorithms perform stati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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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/10218736/ |
| _version_ | 1797736972826968064 |
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| author | Zhiqi Zhang Lu Wei Shao Xiang Guangqi Xie Chuang Liu Mingyuan Xu |
| author_facet | Zhiqi Zhang Lu Wei Shao Xiang Guangqi Xie Chuang Liu Mingyuan Xu |
| author_sort | Zhiqi Zhang |
| collection | DOAJ |
| description | Onboard real-time processing of remote sensing satellites is an important means of rapidly obtaining information, and the fusion processing of panchromatic and multispectral data is of great significance for optical satellites. In order to ensure the effect, most traditional algorithms perform statistical analysis or transformation on the entire image first and then perform subsequent processing. There are problems such as high algorithm complexity and resource occupation, and it is difficult to apply to onboard scenarios where the volume and power consumption are strictly limited. Aiming at the requirements of onboard fusion, a real-time processing approach for high-resolution optical satellites is proposed. First, through the implementation of real-time geometric positioning, ROI extraction is completed while the camera is imaging, avoiding the disadvantages of traditional methods for processing large amounts of data; then, based on the principle of object-space consistency, by fine-tuning virtual sensor parameters, the registration of a panchromatic multispectral image is completed in the sensor correction step, so that the relative accuracy of the two can meet the fusion requirements, and time-consuming pixel-level registration processing is avoided; finally, according to the characteristics of the algorithms and embedded hardware, an efficient algorithm mapping strategy is formulated, and deep optimization is implemented to achieve a significant improvement in performance. Experiments show that the performance of this method is improved by 156.23 times compared with the traditional method. Moreover, after building a parallel pipeline, it can meet the real-time fusion processing requirement of completing a 5000 × 5000 pixels ROI area every 2.4 s. |
| first_indexed | 2024-03-12T13:21:39Z |
| format | Article |
| id | doaj.art-b53548dc5849408fa5177f81aa8627c9 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-12T13:21:39Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-b53548dc5849408fa5177f81aa8627c92023-08-25T23:00:15ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01167636766110.1109/JSTARS.2023.330523110218736Task-Driven Onboard Real-Time Panchromatic Multispectral Fusion Processing Approach for High-Resolution Optical Remote Sensing SatelliteZhiqi Zhang0https://orcid.org/0000-0003-1914-9430Lu Wei1https://orcid.org/0009-0006-4363-1672Shao Xiang2https://orcid.org/0000-0002-2797-1937Guangqi Xie3https://orcid.org/0000-0002-0292-1626Chuang Liu4https://orcid.org/0009-0001-8246-3417Mingyuan Xu5https://orcid.org/0009-0006-2374-2957School of Computer Science, Hubei University of Technology, Wuhan, ChinaSchool of Information Science and Engineering, Wuchang Shouyi University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, ChinaOnboard real-time processing of remote sensing satellites is an important means of rapidly obtaining information, and the fusion processing of panchromatic and multispectral data is of great significance for optical satellites. In order to ensure the effect, most traditional algorithms perform statistical analysis or transformation on the entire image first and then perform subsequent processing. There are problems such as high algorithm complexity and resource occupation, and it is difficult to apply to onboard scenarios where the volume and power consumption are strictly limited. Aiming at the requirements of onboard fusion, a real-time processing approach for high-resolution optical satellites is proposed. First, through the implementation of real-time geometric positioning, ROI extraction is completed while the camera is imaging, avoiding the disadvantages of traditional methods for processing large amounts of data; then, based on the principle of object-space consistency, by fine-tuning virtual sensor parameters, the registration of a panchromatic multispectral image is completed in the sensor correction step, so that the relative accuracy of the two can meet the fusion requirements, and time-consuming pixel-level registration processing is avoided; finally, according to the characteristics of the algorithms and embedded hardware, an efficient algorithm mapping strategy is formulated, and deep optimization is implemented to achieve a significant improvement in performance. Experiments show that the performance of this method is improved by 156.23 times compared with the traditional method. Moreover, after building a parallel pipeline, it can meet the real-time fusion processing requirement of completing a 5000 × 5000 pixels ROI area every 2.4 s.https://ieeexplore.ieee.org/document/10218736/Onboardoptical remote sensingpanchromatic multispectral fusionreal-timetask-driven |
| spellingShingle | Zhiqi Zhang Lu Wei Shao Xiang Guangqi Xie Chuang Liu Mingyuan Xu Task-Driven Onboard Real-Time Panchromatic Multispectral Fusion Processing Approach for High-Resolution Optical Remote Sensing Satellite IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Onboard optical remote sensing panchromatic multispectral fusion real-time task-driven |
| title | Task-Driven Onboard Real-Time Panchromatic Multispectral Fusion Processing Approach for High-Resolution Optical Remote Sensing Satellite |
| title_full | Task-Driven Onboard Real-Time Panchromatic Multispectral Fusion Processing Approach for High-Resolution Optical Remote Sensing Satellite |
| title_fullStr | Task-Driven Onboard Real-Time Panchromatic Multispectral Fusion Processing Approach for High-Resolution Optical Remote Sensing Satellite |
| title_full_unstemmed | Task-Driven Onboard Real-Time Panchromatic Multispectral Fusion Processing Approach for High-Resolution Optical Remote Sensing Satellite |
| title_short | Task-Driven Onboard Real-Time Panchromatic Multispectral Fusion Processing Approach for High-Resolution Optical Remote Sensing Satellite |
| title_sort | task driven onboard real time panchromatic multispectral fusion processing approach for high resolution optical remote sensing satellite |
| topic | Onboard optical remote sensing panchromatic multispectral fusion real-time task-driven |
| url | https://ieeexplore.ieee.org/document/10218736/ |
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