6533b7dcfe1ef96bd12728e3
RESEARCH PRODUCT
Optimizing Kernel Ridge Regression for Remote Sensing Problems
Valero LaparraGonxalo Mateo-garciaLuis Gómez-chovasubject
Computer science0211 other engineering and technologiesHyperspectral imagingContext (language use)Basis function02 engineering and technology01 natural sciencesData set010104 statistics & probabilityKernel (linear algebra)Kernel methodKernel (statistics)Radial basis function kernel0101 mathematics021101 geological & geomatics engineeringReproducing kernel Hilbert spaceRemote sensingdescription
Kernel methods have been very successful in remote sensing problems because of their ability to deal with high dimensional non-linear data. However, they are computationally expensive to train when a large amount of samples are used. In this context, while the amount of available remote sensing data has constantly increased, the size of training sets in kernel methods is usually restricted to few thousand samples. In this work, we modified the kernel ridge regression (KRR) training procedure to deal with large scale datasets. In addition, the basis functions in the reproducing kernel Hilbert space are defined as parameters to be also optimized during the training process. This extends the number of free parameters from two (in the standard KRR with an RBF kernel) to more than fifty thousand in our experiments. The effectiveness of the proposal is illustrated in the problem of surface temperature estimation from MetOp-IASI hyperspectral infrared sounding data. The data set used contains more than one million samples, but the proposed method could potentially be trained with much more data.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-07-01 | IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium |