Remote Sensing of Atmospheric Aerosol Distributions Using Supervised Texture Classification

This thesis presents a new technique to identify a 2D mask showing the extent of particulate aerosol distributions in satellite imagery. This technique uses a supervised texture classification approach, and utilises data from two distinct satellite sources. The vertical feature mask (VFM) product from the CALIPSO lidar, provides an accurate description of the aerosol content of the atmosphere but has a limited footprint and coverage. The CALIPSO VFM is used to provide training data in order to form classifiers to be applied to other imagery, namely data from the spinning enhanced visible and infrared imager (SEVIRI) on the MSG satellite. The output from the classification is a 2D mask representing the locations of the particulate aerosol of interest within the SEVIRI image. This approach has been demonstrated on test cases over land and ocean, and shows a good agreement with other techniques for the detection of particulate aerosol. However, the supervised texture approach provides outputs at a higher resolution than the existing methods and the same approach is applicable over land and ocean and therefore shows the advantages compared to the current techniques. Furthermore, the coverage of the approach can be further extended using signature extension and chain classification. Signature extension was applied to one of the test cases to monitor the same geographical region with temporal extension away from the initial supervised classification. The experiments showed that it was possible to extend the coverage for ±90 minutes from the original classification and indicates the possibility of greater extension over larger temporal windows.