A DATA MINING APPROACH FOR MULTIVARIATE OUTLIER DETECTION IN HETEROGENEOUS 2D POINT CLOUDS: AN APPLICATION TO POST-PROCESSING OF MULTI-TEMPORAL INSAR RESULTS

Abstract

Thresholding on coherence is a common practice for identifying the surface scatterers that are less affected by decorrelation noise during post-processing and visualisation of the results from multi-temporal InSAR techniques. Simple selection of the points with coherence greater than a specific value is, however, challenged by the presence of spatial dependence among observations. If the discrepancies in the areas of moderate coherence share similar behaviour, it appears important to take into account their spatial correlation for correct inference. Low coherence areas thus could serve as clear indicators of measurement noise or imperfections in mathematical models. Once exhibiting properties of statistical similarity, they allow for detection of observations that could be considered as outliers and trimmed from the dataset. In this paper we propose an approach based on renowned data mining and exploratory data analysis procedures for mitigating the impact of outlying observations in the final results.