Visualisation and detection using 3-D laser radar and hyperspectral sensors

Sammanfattning: The main goal of this thesis is to show the strength of combining datasets from two different types of sensors to find anomalies in their data. The sensors used in this thesis are a hyperspectral camera and a scanning 3-D laser. The report can be divided into two main parts. The first part discusses the properties of one of the datasets and how these are used to isolate anomalies. An issue to deal with here is not only what properties to look at, but how to make the process automatic. The information retained from the first dataset is then used to make intelligent choices in the second dataset. Again, one of the challenges is to make this process automatic and accurate. The second part of the project consists of presenting the results in a way that gives the most information to the user. This is done with a graphical user interface that allows the user to manipulate the way the result is presented. The conclusion of this project is that the information from the combined sensor datasets gives better results than the sum of the information from the individual datasets. The key of success is to play to the strengths of the datasets in question. An important block of the work in this thesis, the calibration of the two sensors, was completed by Kevin Chan as his thesis work in Electrical Engineering at the University of Lund. His contribution gave access to calibrated data that supported the results presented in this report.