Journal of Applied Remote Sensing · 2011 · 13 citations · 13 references
ComputingGeoinformaticsComputer VisionFeature EngineeringRemote SensingDigital Image ProcessingImage RegistrationMultispectral ImagingRemote Sensing Image AnalysisRemote Sensing ImageryFeature MatchingHigh-resolution Satellite Image AnalysisMultispectral Image AnalysisReflectanceRemote Sensing Image FeaturesScale-invariant Feature TransformScale-invariant Feature Transform (Sift)Speeded-up Robust Features (Surf)
Satellite images captured in different spectral bands might exhibit nonlinear intensity changes at the corresponding spatial locations due to the different reflectance responses for these bands. This affects the image registration performance negatively as the corresponding features might have different properties in different bands. We propose a modification to the widely used scale invariant feature transform (SIFT) method to increase the correct feature matching ratio and to decrease the computation time of this algorithm for the multispectral satellite images. We also apply scale restriction to SIFT and speeded up robust features (SURF) algorithms to increase the correct match ratio. We present test results for variations of SIFT and SURF algorithms. The results show the effectiveness of the proposed improvements compared to the other SIFT- and SURF-based methods.
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Histograms of Oriented Gradients for Human Detection
Navneet Dalal, Bill Triggs · 2005 · 31.9K citations · Full text
Object recognition from local scale-invariant features
David Lowe · 1999 · 16.2K citations
Speeded-Up Robust Features (SURF)
Herbert Bay, Andreas Ess, Tinne Tuytelaars et al. · Computer Vision and Image Understanding · 2008 · 13.4K citations