Research Article
Deep Learning Based Hyperspectral Images Analysis for Shrimp Contaminated Detection
@INPROCEEDINGS{10.1007/978-3-030-63083-6_15, author={Minh-Hieu Nguyen and Xuan-Huyen Nguyen-Thi and Cong-Nguyen Pham and Ngoc C. L\"{e} and Huy-Dung Han}, title={Deep Learning Based Hyperspectral Images Analysis for Shrimp Contaminated Detection}, proceedings={Industrial Networks and Intelligent Systems. 6th EAI International Conference, INISCOM 2020, Hanoi, Vietnam, August 27--28, 2020, Proceedings}, proceedings_a={INISCOM}, year={2020}, month={11}, keywords={Hyperspectral Imaging Abnormality classification t-SNE Deep neural network}, doi={10.1007/978-3-030-63083-6_15} }- Minh-Hieu Nguyen
Xuan-Huyen Nguyen-Thi
Cong-Nguyen Pham
Ngoc C. Lê
Huy-Dung Han
Year: 2020
Deep Learning Based Hyperspectral Images Analysis for Shrimp Contaminated Detection
INISCOM
Springer
DOI: 10.1007/978-3-030-63083-6_15
Abstract
In this paper, a deep learning based hyperspectral image analysis for detecting contaminated shrimp is proposed. The ability of distinguishing shrimps into two classes: clean and contaminated shrimps is visualized by t-distributed Stochastic Neighbor Embedding (t-SNE) using spectral feature data. Using only some small data set of hyperspectral images of shrimps, a simple processing technique is applied to generate enough data for training a deep neural network (DNN) with high reliability. Our results attain the accuracy of 98% and F1-score over 94%. This works confirms that with only few data samples, Hyperspectral Imaging processing technique together with DNN can be used to classify abnormality in agricultural productions like shrimp.
