Enhancing Dust Storm Detection Using PCA Based Data Fusion

摘要

Principal Component Analysis (PCA) has been widely used as a data reduction technique to overcome the curse of dimensionality. In this research we show a different use for PCA technique as a tool for data fusion. PCA as a data fusion technique is performed over the Multiangle Imaging Spectroradiometer (MISR) data, studying dust storms to better serve their identification. The multi-angle viewing capability of MISR is used to enhance our understanding of the Earth's environment that includes climate particularly of atmosphere and of land surfaces. In this research the multi angle MISR images clearly show a dust storm over the Liaoning region of China as well as parts of northern and western Korea on April 8, 2002. PCA is used to combine the obtained information from the different angle views and frequency bands of MISR datasets. Performing K-means clustering on the original and the assimilated products apply a quantitative measure that is introduced. Upon classifying the first 4 principal components (PCs) having 95% of the information content similar results were obtained as compared to the classification using original datasets.