| Summary: | This final report of Final Year Project seeks to provide an elaborate account of the process of developing a MATLAB algorithm to achieve precise automatic cloud detection capabilities. This report will cover the contributions of the author to this project for the last two semesters. Overall, this project aims to distinguish real clouds from white areas on optical satellite images by neural network, so as to develop an intelligent and convenient MATLAB algorithm for reliable cloud detection and assessment for automated satellite imagery selection. This report will firstly introduce the MATLAB toolbox for cloud detection and assessment containing the procedure of image processing as well as the preparation steps for neural network building, followed by the analysis of two imaging elements used for neural network training. Elements such as RGB, HSV and histogram are analyzed, providing readers with the basic knowledge of how the elements work as attributes in image detection. Finally, the result of cloud detection and automation of assessment will be presented. The report will then reveal two complete case studies of this project, which emphasized on one specific place in the United States and one in the Arctic Ocean, using the developed MATLAB toolbox. The report will then proceed to a comparison of the three methods of cloud detection and assessment, thus discussing about the advantage and disadvantage of the three. Last but not least, this report will end with the challenges faced during the implementation of this project and also the recommendations that can be further explored on the field of study in the future. Bachelor of Engineering
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