Nonlinear Cook distance for Anomalous Change Detection
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Change DetectionSimilar Papers 제목 키워드 기반
Kernel Anomalous Change Detection for Remote Sensing Imagery
Anomalous change detection (ACD) is an important problem in remote sensing image processing. Detecting not only pervasive but also anomalous or extreme changes has many applications for which methodologies are available.…
Change DetectionvalidHybrid Cryptocurrency Pump and Dump Detection
Increasingly growing Cryptocurrency markets have become a hive for scammers to run pump and dump schemes which is considered as an anomalous activity in exchange markets. Anomaly detection in time series is challenging s…
Anomaly DetectionTime SeriesTime Series AnalysisAnomalous Sound Detection Using a Binary Classification Model and Class Centroids
An anomalous sound detection system to detect unknown anomalous sounds usually needs to be built using only normal sound data. Moreover, it is desirable to improve the system by effectively using a small amount of anomal…
Binary ClassificationClassificationMetric LearningMulti-Task LearningDynamic change-point detection using similarity networks
From a sequence of similarity networks, with edges representing certain similarity measures between nodes, we are interested in detecting a change-point which changes the statistical property of the networks. After the c…
Change DetectionChange Point DetectionCommunity DetectionInterdependency Matters: Graph Alignment for Multivariate Time Series Anomaly Detection
Anomaly detection in multivariate time series (MTS) is crucial for various applications in data mining and industry. Current industrial methods typically approach anomaly detection as an unsupervised learning task, aimin…
Anomaly DetectionTime SeriesTime Series Anomaly Detection