Cloud detection and assessment for optical satellite imagery
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 pr...
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ftnanyangtu:oai:dr.ntu.edu.sg:10356/75025 2023-07-30T04:01:57+02:00 Cloud detection and assessment for optical satellite imagery Li, Xintong Lu Yilong School of Electrical and Electronic Engineering 2018 70 p. application/pdf http://hdl.handle.net/10356/75025 en eng http://hdl.handle.net/10356/75025 Nanyang Technological University DRNTU::Engineering Final Year Project (FYP) 2018 ftnanyangtu 2023-07-14T00:21:37Z 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 Other/Unknown Material Arctic Arctic Ocean DR-NTU (Digital Repository at Nanyang Technological University, Singapore) Arctic Arctic Ocean |
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DR-NTU (Digital Repository at Nanyang Technological University, Singapore) |
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ftnanyangtu |
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English |
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DRNTU::Engineering |
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DRNTU::Engineering Li, Xintong Cloud detection and assessment for optical satellite imagery |
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DRNTU::Engineering |
description |
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 |
author2 |
Lu Yilong School of Electrical and Electronic Engineering |
format |
Other/Unknown Material |
author |
Li, Xintong |
author_facet |
Li, Xintong |
author_sort |
Li, Xintong |
title |
Cloud detection and assessment for optical satellite imagery |
title_short |
Cloud detection and assessment for optical satellite imagery |
title_full |
Cloud detection and assessment for optical satellite imagery |
title_fullStr |
Cloud detection and assessment for optical satellite imagery |
title_full_unstemmed |
Cloud detection and assessment for optical satellite imagery |
title_sort |
cloud detection and assessment for optical satellite imagery |
publishDate |
2018 |
url |
http://hdl.handle.net/10356/75025 |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Arctic Arctic Ocean |
genre_facet |
Arctic Arctic Ocean |
op_relation |
http://hdl.handle.net/10356/75025 |
op_rights |
Nanyang Technological University |
_version_ |
1772812683692736512 |