Hyperspectral image classification using meta-heuristics and artificial neural network.
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| Title: | Hyperspectral image classification using meta-heuristics and artificial neural network. |
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| Authors: | Dhingra, Sakshi1 (AUTHOR) sakshi24.dhingra@gmail.com, Kumar, Dharminder1 (AUTHOR) dr_dk_kumar_02@yahoo.com |
| Source: | Journal of Information & Optimization Sciences. Dec2022, Vol. 43 Issue 8, p2167-2179. 13p. |
| Subjects: | Feature selection, Metaheuristic algorithms, Global optimization, Spectral imaging, Genetic algorithms, Data structures, Classification |
| Abstract: | Hyperspectral images usually comprise several continuous spectral bands that represent the category of similar objects or material within the captured scene. These high-dimensional data structures have a high level of correlation and possess unique information that can be used for precise image classification. The precise selection of useful features from these high dimensional band information is very important to reduce the challenge of hyper spectral image classification approaches. Nowadays, metaheuristic algorithms are immensely utilized as a promising tool for hyperspectral image classification. In the present research work, hyperspectral images are classified with the various combinations of meta-heuristic approaches and the neural network including the mostly used Cuckoo Search (CS) optimization algorithm to resolve the global optimization search problems considering the improvement needed in image classification. Further, the strength of CS is improved using the integration of the Genetic Algorithm (GA) fitness function within the CS. The feature selection is performed by the hybrid CS and GA algorithm and the optimized features are then fed to ANN for training and classification. The paper has shown a comparative analysis of various meta heuristics techniques with ANN on parameters like kappa coefficient, Class accuracy and overall Accuracy and the designed algorithms are tested on the Indian Pines dataset. The proposed CS and GA with ANN outperformed the two already existing works with an overall average accuracy of 97.30% and a kappa coefficient of 0.9760. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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