Academic Journal
Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization
| العنوان: | Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization |
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| المؤلفون: | Raju Anand, Sathishkumar Samiaappan, Shanmugham Veni, Ethan Worch, Meilun Zhou |
| المصدر: | Journal of Imaging, Vol 8, Iss 126, p 126 (2022) |
| بيانات النشر: | MDPI AG |
| سنة النشر: | 2022 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | moth–flame, optimization, hyperspectral image, classifier, particle swarm, genetic algorithm, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | In this research, we study a new metaheuristic algorithm called Moth–Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statistical classifier is high. Thus, the problem is to select a subset of bands without compromising the classification accuracy. One of the ways to solve this problem is to model an objective function that measures class separability and utilize it to arrive at a subset of bands. In this research, we studied MFO to select optimal spectral bands for classification. MFO is inspired by the behavior of moths with respect to flames, which is the navigation method of moths in nature called transverse orientation. In MFO, a moth navigates the search space through a process called transverse orientation by keeping a constant angle with the Moon, which is a compelling strategy for traveling long distances in a straight line, considering that the Moon’s distance from the moth is considerably long. Our research tested MFO on three benchmark hyperspectral datasets—Indian Pines, University of Pavia, and Salinas. MFO produced an Overall Accuracy (OA) of 88.98%, 94.85%, and 97.17%, respectively, on the three datasets. Our experimental results indicate that MFO produces better OA and Kappa when compared to state-of-the-art band selection algorithms such as particle swarm optimization, grey wolf, cuckoo search, and genetic algorithms. The analysis results prove that the proposed approach effectively addresses the spectral band selection problem and provides a high classification accuracy. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2313-433X |
| Relation: | https://www.mdpi.com/2313-433X/8/5/126; https://doaj.org/toc/2313-433X; https://doaj.org/article/7377017c50544e4c89c664391287dd4f |
| DOI: | 10.3390/jimaging8050126 |
| الاتاحة: | https://doi.org/10.3390/jimaging8050126 https://doaj.org/article/7377017c50544e4c89c664391287dd4f |
| رقم الانضمام: | edsbas.D6D9860 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 2313433X |
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| DOI: | 10.3390/jimaging8050126 |