A Novel Approach for Hyperspectral Image Classification using Bat Algorithm to Optimize a CNN Classifier

We propose a novel hybrid classifier for hyperspectral images using Bat Algorithm (BA) to optimize the architecture of a Convolutional Neural Network (CNN). BA is applied by minimization of the CNN cross-entropy on the validation set. The objective function is represented as a matrix containing the...

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Bibliographic Details
Published in2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) pp. 1 - 6
Main Authors Rujan, Liviu, Neagoe, Victor-Emil
Format Conference Proceeding
LanguageEnglish
Published IEEE 29.06.2023
Subjects
Bat Algorithm (BA)
BAT-CNN classifier
Classification algorithms
Computational modeling
Computer architecture
Convolutional neural networks
Convolutional Neural Networks (CNNs)
hyperspectral image classification
Linear programming
Neural networks
Training
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Summary:We propose a novel hybrid classifier for hyperspectral images using Bat Algorithm (BA) to optimize the architecture of a Convolutional Neural Network (CNN). BA is applied by minimization of the CNN cross-entropy on the validation set. The objective function is represented as a matrix containing the parameters which define the CNN architecture. The proposed BAT-CNN classifier is evaluated on three hyperspectral datasets: Indian Pines, Pavia University and Salinas. The experiments lead to a better accuracy for the proposed hybrid classifier by comparison to the standalone CNN classifier, for each of the three considered datasets.
DOI:10.1109/ECAI58194.2023.10193859