Classification of damaged vegetation areas using convolutional neural network over satellite images
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| Լեզու: | English |
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Brac University
2021
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| Առցանց հասանելիություն: | http://hdl.handle.net/10361/15364 |
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10361-153642022-01-26T10:18:16Z Classification of damaged vegetation areas using convolutional neural network over satellite images Haque, Samiha Rahman, Nazibur Mostakim, Moin Department of Computer Science and Engineering, Brac University Machine Learning CNN Convolutional Neural Network K-Means Change Detection Multispectral Image Transfer Learning ResNet-50 Sentinel-2 Machine Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 60-63). Forests and wild vegetation have always been highly significant natural resources throughout history and play a crucial role in keeping the climate and ecosystems well balanced. Over the years, there has been a growing decline in the forest areas all over the world due to hurricanes, landslides and most notably forest fires which have been directly linked to global warming. Very recently, large fires in Brazilian Amazon, Australia, Thailand USA have had devastating effects, destroying millions of acres of vegetation. There has been a rise in frequency and severity of such forest disasters over the years and therefore, understanding the scale of damage on the forest areas is very important. For this purpose, multispectral high resolution remote sensing data from satellites such as Sentinel-2 can be used to extract spatial information before and after the disaster, identify the damaged areas and evaluate the amount damaged. In this research, we construct a dataset from the difference image of disaster struck vegetation areas and a pre-trained ResNet-50 is used to produce flattened feature maps from the difference image patches. These feature maps are then labelled using the K-Means clustering algorithm, thus creating a complete labelled dataset which is used to train two CNN models to classify the areas into undamaged and damaged classes for Model-1 with 89.77% test accuracy and undamaged, mildly damaged, and severely damaged classes for Model-2 with 85.69% test accuracy. Model-1 recorded 89.77% in micro F1-score, 76.99% for kappa score and 0.10 for overall error and Model-2 had 85.69%, 72.87% and 0.14 values in micro F1-score, kappa score and overall error respectively. Samiha Haque Nazibur Rahman B. Computer Science 2021-10-18T08:47:58Z 2021-10-18T08:47:58Z 2021 2021-01 Thesis ID 17101218 ID 17101317 http://hdl.handle.net/10361/15364 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 63 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Machine Learning CNN Convolutional Neural Network K-Means Change Detection Multispectral Image Transfer Learning ResNet-50 Sentinel-2 Machine Learning |
| spellingShingle |
Machine Learning CNN Convolutional Neural Network K-Means Change Detection Multispectral Image Transfer Learning ResNet-50 Sentinel-2 Machine Learning Haque, Samiha Rahman, Nazibur Classification of damaged vegetation areas using convolutional neural network over satellite images |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Mostakim, Moin |
| author_facet |
Mostakim, Moin Haque, Samiha Rahman, Nazibur |
| format |
Thesis |
| author |
Haque, Samiha Rahman, Nazibur |
| author_sort |
Haque, Samiha |
| title |
Classification of damaged vegetation areas using convolutional neural network over satellite images |
| title_short |
Classification of damaged vegetation areas using convolutional neural network over satellite images |
| title_full |
Classification of damaged vegetation areas using convolutional neural network over satellite images |
| title_fullStr |
Classification of damaged vegetation areas using convolutional neural network over satellite images |
| title_full_unstemmed |
Classification of damaged vegetation areas using convolutional neural network over satellite images |
| title_sort |
classification of damaged vegetation areas using convolutional neural network over satellite images |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15364 |
| work_keys_str_mv |
AT haquesamiha classificationofdamagedvegetationareasusingconvolutionalneuralnetworkoversatelliteimages AT rahmannazibur classificationofdamagedvegetationareasusingconvolutionalneuralnetworkoversatelliteimages |
| _version_ |
1814308688945479680 |