Nonlinear Cook distance for Anomalous Change Detection
Computer Vision and Pattern Recognition
2020-12-24 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.2012.12307,
title = {Nonlinear Cook distance for Anomalous Change Detection},
author = {José A. Padrón Hidalgo and Adrián Pérez-Suay and Fatih Nar and Gustau Camps-Valls},
journal= {arXiv preprint arXiv:2012.12307},
year = {2020}
}