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Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
Abstrakt
Satellite image classification is widely used in various real-time applications, such as the military, geospatial surveys, surveillance and environmental monitoring. Therefore, the effective classification of satellite images is required to improve classification accuracy. In this paper, the combination of Hierarchical Framework and Ensemble Learning (HFEL) and optimal feature selection is proposed for the precise identification of satellite images. The HFEL uses three different types of Convolutional Neural Networks (CNN), namely AlexNet, LeNet-5 and a residual network (ResNet), to extract the appropriate features from images of the hierarchical framework. Additionally, the optimal features from the feature set are extracted using the Correlation Coefficient-Based Gravitational Search Algorithm (CCGSA). Further, the Multi Support Vector Machine (MSVM) is used to classify the satellite images by extracted features from the fully connected layers of the CNN and selected features of the CCGSA. Hence, the combination of HFEL and CCGSA is used to obtain the precise classification over different datasets such as the SAT-4, SAT-6 and Eurosat datasets. The performance of the proposed HFEL–CCGSA is analyzed in terms of accuracy, precision and recall. The experimental results show that the HFEL–CCGSA method provides effective classification over the satellite images. The classification accuracy of the HFEL–CCGSA method is 99.99%, which is high when compared to AlexNet, LeNet-5 and ResNet.
Cytowania
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5 7
CrossRef
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0
Web of Science
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5 7
Scopus
Autorzy (5)
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Kowsalya Thiagarajan
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Mukunthan Manapakkam Anandan
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Parameshachari Bidare Divakarachari
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Hemalatha Kivudujogappa Lingappa
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Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/rs13214351
- Licencja
-
otwiera się w nowej karcie
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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Remote Sensing
nr 13,
ISSN: 2072-4292 - Język:
- angielski
- Rok wydania:
- 2021
- Opis bibliograficzny:
- Thiagarajan K., Manapakkam Anandan M., Stateczny A., Bidare Divakarachari P., Kivudujogappa Lingappa H.: Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm// Remote Sensing -Vol. 13,iss. 21 (2021), s.4351-
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/rs13214351
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 92 razy