Abstract
Recently, hyperspectral image (HSI) classification is very important topic in remote sensing field. During the past, many deep learning models have been used for classifying HSI. Among these models, convolutional neural network (CNN) got immense popularity and shown remarkable performance. Instead of this, CNN-based HSI classification methods suffer a lot due to overfitting. To reduce the overfitting, different regularization techniques like batch normalization, L2 regularization, dropout have been utilized. Among various regularization techniques, dropout is very common and effective. Therefore, in this paper, we have shown the effect of dropout technique on CNN-based model for HSI classification. The experiment has been performed on three widely used HSI datasets to validate our model. The observed results have shown 5%, 3%, and 1% improvement on Indian Pines, University of Pavia and Salinas dataset, respectively when dropout was applied.
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Bera, S., Shrivastava, V.K. (2023). Effect of Dropout on Convolutional Neural Network for Hyperspectral Image Classification. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_12
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DOI: https://doi.org/10.1007/978-981-99-3656-4_12
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