6533b7d6fe1ef96bd126672b
RESEARCH PRODUCT
Nonlinear Cook distance for Anomalous Change Detection
Adrian Perez-suayFatih NarJose A. Padron HidalgoGustau Camps-vallssubject
FOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceComputer Vision and Pattern Recognition (cs.CV)Multispectral imageComputer Science - Computer Vision and Pattern Recognition0211 other engineering and technologies02 engineering and technologyMeasure (mathematics)Machine Learning (cs.LG)Kernel (linear algebra)symbols.namesake0502 economics and businessCook's distance021101 geological & geomatics engineering050208 financePixelbusiness.industry05 social sciencesPattern recognitionNonlinear systemFourier transformKernel (image processing)Computer Science::Computer Vision and Pattern RecognitionsymbolsArtificial intelligencebusinessChange detectiondescription
In this work we propose a method to find anomalous changes in remote sensing images based on the chronochrome approach. A regressor between images is used to discover the most {\em influential points} in the observed data. Typically, the pixels with largest residuals are decided to be anomalous changes. In order to find the anomalous pixels we consider the Cook distance and propose its nonlinear extension using random Fourier features as an efficient nonlinear measure of impact. Good empirical performance is shown over different multispectral images both visually and quantitatively evaluated with ROC curves.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-12-08 |