6533b822fe1ef96bd127ced3
RESEARCH PRODUCT
Remote Sensing Image Classification with Large Scale Gaussian Processes
Adrian Perez-suayPablo Morales-alvarezGustau Camps-vallsRafael Molinasubject
FOS: Computer and information sciences010504 meteorology & atmospheric sciencesComputer scienceMultispectral image0211 other engineering and technologiesMachine Learning (stat.ML)02 engineering and technologyLand cover01 natural sciencesStatistics - ApplicationsMachine Learning (cs.LG)Kernel (linear algebra)Bayes' theoremsymbols.namesakeStatistics - Machine LearningApplications (stat.AP)Electrical and Electronic EngineeringGaussian process021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingContextual image classificationArtificial neural networkData stream miningProbabilistic logicSupport vector machineComputer Science - LearningKernel (image processing)symbolsGeneral Earth and Planetary Sciencesdescription
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are currently available. A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines. However, its computational cost is prohibitive for large scale applications, and constitutes the main obstacle precluding wide adoption. This paper tackles this problem by introducing two novel efficient methodologies for Gaussian Process (GP) classification. We first include the standard random Fourier features approximation into GPC, which largely decreases its computational cost and permits large scale remote sensing image classification. In addition, we propose a model which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones within a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data. Excellent empirical results support the proposal in both computational cost and accuracy.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-10-02 |