This paper presents a Convolutional Neural Network (CNN)-based spatial classifier to classify hyperspectral images for wood recognition. The spatial classifier is built by adapting the input and output units of Cifar10Net, a conventional image classifier that accepts three-band images as input. Obtained results in terms of accuracy and training time show that the proposed classifier can be trained using few training data, and few computational resources.
A Lightweight Approach for Wood Hyperspectral Images Classification
Confalonieri R.;
2021
Abstract
This paper presents a Convolutional Neural Network (CNN)-based spatial classifier to classify hyperspectral images for wood recognition. The spatial classifier is built by adapting the input and output units of Cifar10Net, a conventional image classifier that accepts three-band images as input. Obtained results in terms of accuracy and training time show that the proposed classifier can be trained using few training data, and few computational resources.File in questo prodotto:
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