Well-Distributed Feature Extraction for Image Registration Using Histogram Matching
Image registration is a spatial alignment of corresponding images of the same scene acquired from different views, sensors, and time intervals. Especially, satellite image registration is a challenging task due to the high resolution of images. In addition, demands for high resolution satellite imag...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/17/3487 |
| _version_ | 1818561730292219904 |
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| author | Muhammad Tariq Mahmood Ik Hyun Lee |
| author_facet | Muhammad Tariq Mahmood Ik Hyun Lee |
| author_sort | Muhammad Tariq Mahmood |
| collection | DOAJ |
| description | Image registration is a spatial alignment of corresponding images of the same scene acquired from different views, sensors, and time intervals. Especially, satellite image registration is a challenging task due to the high resolution of images. In addition, demands for high resolution satellite imagery are increased for more detailed and precise information in land planning, urban planning, and Earth observation. Commonly, feature-based methods are applied for image registration. In these methods, first control or key points are detected using feature detector such as scale-invariant feature transform (SIFT). The numbers and the distribution of these control points are important for the remaining steps of registration. These methods provide reasonable performance; however, they suffer from high computational cost and irregular distribution of control points. To overcome these limitations, we propose an area-based registration method using histogram matching and zero mean normalized cross-correlation (ZNCC). In multi-spectral satellite images, first, different spectral responses are adjusted by using histogram matching. Then, ZNCC is utilized to extract well-distributed control points. In addition, fast Fourier transform (FFT) and block-wise processing are applied to reduce the computational cost. The proposed method is evaluated through various input datasets. The results demonstrate its efficacy and accuracy in image registration. |
| first_indexed | 2024-12-14T00:54:40Z |
| format | Article |
| id | doaj.art-402f18d2e13d4617bddff7192f0441e4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-14T00:54:40Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-402f18d2e13d4617bddff7192f0441e42022-12-21T23:23:38ZengMDPI AGApplied Sciences2076-34172019-08-01917348710.3390/app9173487app9173487Well-Distributed Feature Extraction for Image Registration Using Histogram MatchingMuhammad Tariq Mahmood0Ik Hyun Lee1School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolno, Byeogchunmyun, Cheonan 31253, KoreaDepartment of Mechatronics, Korea Polytechnic University, 237 Sangidaehak-ro, Siheung-si, Gyeonggi-do 15073, KoreaImage registration is a spatial alignment of corresponding images of the same scene acquired from different views, sensors, and time intervals. Especially, satellite image registration is a challenging task due to the high resolution of images. In addition, demands for high resolution satellite imagery are increased for more detailed and precise information in land planning, urban planning, and Earth observation. Commonly, feature-based methods are applied for image registration. In these methods, first control or key points are detected using feature detector such as scale-invariant feature transform (SIFT). The numbers and the distribution of these control points are important for the remaining steps of registration. These methods provide reasonable performance; however, they suffer from high computational cost and irregular distribution of control points. To overcome these limitations, we propose an area-based registration method using histogram matching and zero mean normalized cross-correlation (ZNCC). In multi-spectral satellite images, first, different spectral responses are adjusted by using histogram matching. Then, ZNCC is utilized to extract well-distributed control points. In addition, fast Fourier transform (FFT) and block-wise processing are applied to reduce the computational cost. The proposed method is evaluated through various input datasets. The results demonstrate its efficacy and accuracy in image registration.https://www.mdpi.com/2076-3417/9/17/3487image registrationarea-based registrationfeature-based registration |
| spellingShingle | Muhammad Tariq Mahmood Ik Hyun Lee Well-Distributed Feature Extraction for Image Registration Using Histogram Matching Applied Sciences image registration area-based registration feature-based registration |
| title | Well-Distributed Feature Extraction for Image Registration Using Histogram Matching |
| title_full | Well-Distributed Feature Extraction for Image Registration Using Histogram Matching |
| title_fullStr | Well-Distributed Feature Extraction for Image Registration Using Histogram Matching |
| title_full_unstemmed | Well-Distributed Feature Extraction for Image Registration Using Histogram Matching |
| title_short | Well-Distributed Feature Extraction for Image Registration Using Histogram Matching |
| title_sort | well distributed feature extraction for image registration using histogram matching |
| topic | image registration area-based registration feature-based registration |
| url | https://www.mdpi.com/2076-3417/9/17/3487 |
| work_keys_str_mv | AT muhammadtariqmahmood welldistributedfeatureextractionforimageregistrationusinghistogrammatching AT ikhyunlee welldistributedfeatureextractionforimageregistrationusinghistogrammatching |