Published January 1, 2018 | Version v1
Conference paper Open

NONLINEAR COOK DISTANCE FOR ANOMALOUS CHANGE DETECTION

  • 1. Univ Valencia, IPL, Valencia, Spain
  • 2. Konya Food & Agr Univ, Konya, Turkey

Description

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 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.

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