6533b83afe1ef96bd12a7bc6
RESEARCH PRODUCT
Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis
Devis TuiaMichele VolpiGustau Camps-vallssubject
010504 meteorology & atmospheric sciencesFeature extraction0211 other engineering and technologiesRelative spectral alignment02 engineering and technology3107 Atomic and Molecular Physics and Optics01 natural sciencesCross-sensorCanonical correlation analysis1706 Computer Science Applications910 Geography & travelComputers in Earth SciencesEngineering (miscellaneous)021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsGround truthbusiness.industry1903 Computers in Earth SciencesKernel methodsPattern recognitionReal imageAtomic and Molecular Physics and OpticsComputer Science Applications10122 Institute of GeographyTransformation (function)Kernel methodChange detectionFeature extraction2201 Engineering (miscellaneous)Artificial intelligencebusinessCanonical correlationChange detectionCurse of dimensionalitydescription
In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to perform model selection is limited, we propose a completely automatic strategy to select the hyperparameters of the system as well as the dimensionality of the transformed (latent) space. The proposed scheme is fully automatic and allows the use of any change detection algorithm in the transformed latent space. A synthetic problem built from real images and a case study involving a real cross-sensor change detection problem illustrate the capabilities of the proposed method. Results show that the proposed system outperforms the linear baseline and provides accuracies close the ones obtained with a fully supervised strategy. We provide a MATLAB implementation of the proposed method as well as the real cross-sensor data we prepared and employed at https://sites.google.com/site/michelevolpiresearch/codes/cross-sensor.
year | journal | country | edition | language |
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2015-09-01 | ISPRS Journal of Photogrammetry and Remote Sensing |