A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI Images
A band-to-band mis-registration (BBMR) error often occurs in remote sensing (RS) images acquired by multi-spectral push broom spectrometers such as the Sentinel-2 Multi-spectral Instrument (MSI), leading to adverse impacts on the reliability of further RS applications. Although the systematic band-t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/17/3351 |
| _version_ | 1797520961154580480 |
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| author | Tianxin Chen Yongxue Liu |
| author_facet | Tianxin Chen Yongxue Liu |
| author_sort | Tianxin Chen |
| collection | DOAJ |
| description | A band-to-band mis-registration (BBMR) error often occurs in remote sensing (RS) images acquired by multi-spectral push broom spectrometers such as the Sentinel-2 Multi-spectral Instrument (MSI), leading to adverse impacts on the reliability of further RS applications. Although the systematic band-to-band registration conducted during the image production process corrects most BBMR errors, there are still quite a few images being observed with discernible BBMR. Thus, a quick BBMR detection method is needed to assess the quality of online RS products. We here propose a hybrid framework for detecting BBMR between the visible bands in MSI images. This framework comprises three main steps: first, candidate chips are captured based on Google Earth Engine (GEE) spatial analysis functions to shrink the valid areas inside image scenes as potential target chips. The redundant data pertaining to the local operation process are thus narrowed down. Second, spectral abnormal areas are precisely extracted from inside every single chip, excluding the influences of clouds and water surfaces. Finally, the abnormal areas are matched pixel by pixel between bands, and the best-fit coordinates are then determined to compare with tolerance. Here, the proposed method was applied to 71,493 scenes of MSI Level-1C images covering China and its surrounding areas on the GEE platform. From these images, 4356 chips from 442 scenes were detected with inter-band offsets among the visible bands. Further manual visual inspection revealed that the proposed method had an accuracy of 98.07% at the chip scale and 88.46% at the scene scale. |
| first_indexed | 2024-03-10T08:04:52Z |
| format | Article |
| id | doaj.art-608ff7aceb40451fb1f82f37ab12d662 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:04:52Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-608ff7aceb40451fb1f82f37ab12d6622023-11-22T11:07:31ZengMDPI AGRemote Sensing2072-42922021-08-011317335110.3390/rs13173351A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI ImagesTianxin Chen0Yongxue Liu1School of Geography and Ocean Science, Nanjing University, Nanjing 210046, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210046, ChinaA band-to-band mis-registration (BBMR) error often occurs in remote sensing (RS) images acquired by multi-spectral push broom spectrometers such as the Sentinel-2 Multi-spectral Instrument (MSI), leading to adverse impacts on the reliability of further RS applications. Although the systematic band-to-band registration conducted during the image production process corrects most BBMR errors, there are still quite a few images being observed with discernible BBMR. Thus, a quick BBMR detection method is needed to assess the quality of online RS products. We here propose a hybrid framework for detecting BBMR between the visible bands in MSI images. This framework comprises three main steps: first, candidate chips are captured based on Google Earth Engine (GEE) spatial analysis functions to shrink the valid areas inside image scenes as potential target chips. The redundant data pertaining to the local operation process are thus narrowed down. Second, spectral abnormal areas are precisely extracted from inside every single chip, excluding the influences of clouds and water surfaces. Finally, the abnormal areas are matched pixel by pixel between bands, and the best-fit coordinates are then determined to compare with tolerance. Here, the proposed method was applied to 71,493 scenes of MSI Level-1C images covering China and its surrounding areas on the GEE platform. From these images, 4356 chips from 442 scenes were detected with inter-band offsets among the visible bands. Further manual visual inspection revealed that the proposed method had an accuracy of 98.07% at the chip scale and 88.46% at the scene scale.https://www.mdpi.com/2072-4292/13/17/3351band-to-band mis-registration (BBMR)spectral anomaly detectionmulti-spectral instrument (MSI)Google Earth Engine (GEE)hybrid computation |
| spellingShingle | Tianxin Chen Yongxue Liu A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI Images Remote Sensing band-to-band mis-registration (BBMR) spectral anomaly detection multi-spectral instrument (MSI) Google Earth Engine (GEE) hybrid computation |
| title | A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI Images |
| title_full | A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI Images |
| title_fullStr | A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI Images |
| title_full_unstemmed | A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI Images |
| title_short | A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI Images |
| title_sort | quick band to band mis registration detection method for sentinel 2 msi images |
| topic | band-to-band mis-registration (BBMR) spectral anomaly detection multi-spectral instrument (MSI) Google Earth Engine (GEE) hybrid computation |
| url | https://www.mdpi.com/2072-4292/13/17/3351 |
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