A Simple Anomaly Detection for Spectral Imagery Using Co-occurrence Statistics Techniques

Abstract

Abstract-Anomaly detection has always been a hot research field of data mining. Anomaly detection is important in many fields. Automatic determination of the anomaly cluster is often needed to eliminate that anomaly cluster. In this paper, a method has been developed to determine the anomaly regions in satellite image using a data mining algorithm based on the co-occurrence matrix technique in order to determinate that anomaly. Our method consists of four stages, the first stage estimate a number of cluster by co-occurrence matrix, the second stage cluster dataset by automatic clustering algorithm, the third stage detect anomalous clusters by threshold value and the final stage defines clusters, which are lower than threshold value, to be anomalous clusters. The proposed method was tested using data from unknown number of clusters with multispectral satellite image in Thailand. The results from the tests confirm the effectiveness of the proposed method in finding the anomaly regions.

Clustering-Based Color Image Segmentation Using Local Maxima

“…Yetanothermethodwasrecommendedfortheautomaticdeterminationoftheinitializationof thenumberofclustersintheK-meansclusteringapplication (Koonsanit,Jaruskulchai,&Eiumnoh, 2012)usingtheco-occurrencestatisticstechniqueformulti-spectralsatelliteimages.Theproposed methodproducesbetterresultswhencomparedtotheisodataalgorithm.Aniterativesegmentation processsuggested (Synthuja,Preetha,Padma,&John,2012)inwhichthesegmentationprocessstops whentheregionofinterestisseparatedfromtheinputimage.Butthisregionofinterestdiffersfrom applicationtoapplication,andthereexistsnosegmentationalgorithmthatsatisfiesglobalapplications. Acomparativestudyoncolorimagesegmentation,basedonregiongrowingandmergingalgorithms, isdiscussed.Thepapersuggestedanautomaticseededregiongrowingalgorithmforsegmenting colorimages.…”