Automatic Generation of Seamless Mosaics Using Invariant Features
The acquisition of satellite images over a wide area is often carried out across seasons because of satellite orbits and atmospheric conditions (e.g., cloud cover, dust, etc.). This results in spectral mismatch between adjacent scenes as the sun angle and the atmospheric conditions will be different...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/16/3094 |
| _version_ | 1797522251634966528 |
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| author | Prajowal Manandhar Ahmad Jalil Khaled AlHashmi Prashanth Marpu |
| author_facet | Prajowal Manandhar Ahmad Jalil Khaled AlHashmi Prashanth Marpu |
| author_sort | Prajowal Manandhar |
| collection | DOAJ |
| description | The acquisition of satellite images over a wide area is often carried out across seasons because of satellite orbits and atmospheric conditions (e.g., cloud cover, dust, etc.). This results in spectral mismatch between adjacent scenes as the sun angle and the atmospheric conditions will be different for different acquisitions. In this work, we developed an approach to generate seamless mosaics using Scale-Invariant Features Transformation (SIFT). In this process, we make use of the overlapping areas between two adjacent scenes and then map spectral values of one imagery scene to another based on the filtered points detected by SIFT features to create a seamless mosaic. We make use of the Random Sample Consensus (RANSAC) method successively to filter out obtained SIFT points across adjacent tiles and to remove spectral outliers across each band of an image. Several high resolution satellite images acquired with WorldView-2 and Dubaisat-2 satellites, and medium resolution Sentinel-2 satellite imagery are used for experimentation. The experimental results show that the proposed approach can generate good seamless mosaics. Furthermore, Sentinel-2’s level 2A (L2A) product surface reflectance data is used to adjust the spectral values for color consistency. |
| first_indexed | 2024-03-10T08:25:46Z |
| format | Article |
| id | doaj.art-cd889091d43b4e76b692bc7f13db52cb |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:25:46Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-cd889091d43b4e76b692bc7f13db52cb2023-11-22T09:31:41ZengMDPI AGRemote Sensing2072-42922021-08-011316309410.3390/rs13163094Automatic Generation of Seamless Mosaics Using Invariant FeaturesPrajowal Manandhar0Ahmad Jalil1Khaled AlHashmi2Prashanth Marpu3National Space Science and Technology Center, United Arab Emirates University, Al Ain 15551, United Arab EmiratesNational Space Science and Technology Center, United Arab Emirates University, Al Ain 15551, United Arab EmiratesNational Space Science and Technology Center, United Arab Emirates University, Al Ain 15551, United Arab EmiratesSpace Program, Group 42, Abu Dhabi 111999, United Arab EmiratesThe acquisition of satellite images over a wide area is often carried out across seasons because of satellite orbits and atmospheric conditions (e.g., cloud cover, dust, etc.). This results in spectral mismatch between adjacent scenes as the sun angle and the atmospheric conditions will be different for different acquisitions. In this work, we developed an approach to generate seamless mosaics using Scale-Invariant Features Transformation (SIFT). In this process, we make use of the overlapping areas between two adjacent scenes and then map spectral values of one imagery scene to another based on the filtered points detected by SIFT features to create a seamless mosaic. We make use of the Random Sample Consensus (RANSAC) method successively to filter out obtained SIFT points across adjacent tiles and to remove spectral outliers across each band of an image. Several high resolution satellite images acquired with WorldView-2 and Dubaisat-2 satellites, and medium resolution Sentinel-2 satellite imagery are used for experimentation. The experimental results show that the proposed approach can generate good seamless mosaics. Furthermore, Sentinel-2’s level 2A (L2A) product surface reflectance data is used to adjust the spectral values for color consistency.https://www.mdpi.com/2072-4292/13/16/3094seamless mosaicRANSACSIFToutliersblendsurface reflectance |
| spellingShingle | Prajowal Manandhar Ahmad Jalil Khaled AlHashmi Prashanth Marpu Automatic Generation of Seamless Mosaics Using Invariant Features Remote Sensing seamless mosaic RANSAC SIFT outliers blend surface reflectance |
| title | Automatic Generation of Seamless Mosaics Using Invariant Features |
| title_full | Automatic Generation of Seamless Mosaics Using Invariant Features |
| title_fullStr | Automatic Generation of Seamless Mosaics Using Invariant Features |
| title_full_unstemmed | Automatic Generation of Seamless Mosaics Using Invariant Features |
| title_short | Automatic Generation of Seamless Mosaics Using Invariant Features |
| title_sort | automatic generation of seamless mosaics using invariant features |
| topic | seamless mosaic RANSAC SIFT outliers blend surface reflectance |
| url | https://www.mdpi.com/2072-4292/13/16/3094 |
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