6533b838fe1ef96bd12a3bf7
RESEARCH PRODUCT
Learning spatial filters for multispectral image segmentation.
Gustau Camps-vallsRémi FlamaryAlain RakotomamonjyDevis Tuiasubject
Computer Science::Machine LearningMultispectral image0211 other engineering and technologies02 engineering and technology01 natural sciencesRegularization (mathematics)010104 statistics & probability[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]Life ScienceComputer visionSegmentation0101 mathematicsLarge margin method021101 geological & geomatics engineeringMathematicsImage segmentationContextual image classificationPixelbusiness.industryPattern recognitionImage segmentationSupport vector machineComputingMethodologies_PATTERNRECOGNITIONmultispectral imageSpatial FilteringArtificial intelligenceGradient descentbusinessdescription
International audience; We present a novel filtering method for multispectral satel- lite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments car- ried out on multiclass one-against-all classification and tar- get detection show the capabilities of the learned spatial fil- ters.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2010-08-29 |