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- W4367358040 abstract "Convolutional neural networks (CNNs) are an effective technique for hyperspectral image classification. Deep learning models require a large number of labeled and diverse samples to properly train a CNN model. But a training set is often not large enough. Transfer learning can help to overcome the need for training sets. In this paper, six pre-trained CNN models: EfficientNetB0, EfficientNetB7, ResNet50, VGG19, DenseNet121 and DenseNet201 are fine-tuned for hyperspectral image classification. The experiments are carried out on two benchmark images Houston and Kennedy Space Center (KSC). The results show that hyperspectral images can be classified with good accuracy by fine-tuned pre-trained CNN models. As compared to training a model from scratch, fine-tuning takes a small number of epochs. Thus, alleviating the requirement for high-end computing resources. Among the tested models, VGG19 achieves the best accuracy of 95.77% for the KSC image, while EfficientNetB0 performs better than others with 90.79% accuracy for the Houston image." @default.
- W4367358040 created "2023-04-30" @default.
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- W4367358040 date "2023-01-01" @default.
- W4367358040 modified "2023-09-23" @default.
- W4367358040 title "Fine Tuning the Pre-trained Convolutional Neural Network Models for Hyperspectral Image Classification Using Transfer Learning" @default.
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- W4367358040 doi "https://doi.org/10.1007/978-981-19-7892-0_21" @default.
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