Research on visualized data quality control methods of ground object spectrum in Yanzhou mining area

Errors or outliers are prone to be made on account of various accidental factors or system errors in the observation process of ground object spectrums. To find out and remove the data records with outliers from the field spectroscopy data of several typical crops in Yanzhou mining area, we proposed a field spectroscopy data quality controlling theory using cluster analysis methods and box-and-whisker Plots. 4 different cluster analysis methods including Statistical distance, Aitchison distance, Pearson's correlation coefficient and Multidimensional Vector Cosine were used for gross error visualized detection. For the common characteristic bands of different spectrum data, the goal of visualized detection and identification of outliers was achieved by means of the statistical method of box-and-whisker plots. Outliers which were identified can be getting rid of in the use of several self-developed graphic interactive controls based on GDI+ technology. The theory proposed in this paper provided effective quality assurance for in-depth spectroscopy analysis.