6533b862fe1ef96bd12c6e80
RESEARCH PRODUCT
An automatic filtering algorithm for SURF-based registration of remote sensing images
Hanan AnzidAissam BekkariDriss MammassAlamin MansouriGaëtan Le Goïcsubject
RegistrationComputer scienceSatellitesFeature extractionRANSAC filtering0211 other engineering and technologiesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage registration02 engineering and technologyimage matchingRANSACpoint matching stepElectronic mailautomatic filtering algorithmRobustness (computer science)0202 electrical engineering electronic engineering information engineeringOutlier detectionComputer vision[INFO]Computer Science [cs]RobustnessSURF-based registrationImage registration021101 geological & geomatics engineeringRemote sensingimage filteringMeasurementAutomatic filteringviewing geometrybusiness.industrySURF algorithmFeature matchingPoint set registrationRemote sensingfeature pointgeophysical image processingElectronic mail[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Outlierimage registration methodsFeature extraction020201 artificial intelligence & image processingArtificial intelligencebusinessremote sensing imagesdescription
International audience; The registration of remote sensing images has been often a necessary step for further analyses of images taken at different times, different viewing geometry or with different sensors. For this task there exists many approaches. This paper focuses on the feature-based category of image registration methods. Particularly, we propose an improvement of the SURF algorithm on the point matching step. Indeed, in order to achieve a correct registration, a good matching of feature point is required. However The presence of outliers lead to a fail in the registration. Therefore, in this paper, we introduce an efficient method devoted to the detection and removal of such outliers. It's based on an automatic filtering of outliers based on both distance and orientation between feature points. Images from IKONOS and QuickBird satellites are used to evaluate this proposed method, which we compare to classical SURF as well as SURF followed by RANSAC filtering. The results show that our method outperforms the others regarding all assessment criteria.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-05-22 |