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Gong, M., Yang, H. and Zhang, P. (2017) Feature Learning and Change Feature Classification Based on Deep Learning for Ternary Change Detection in SAR Images. ISPRS Journal of Photogrammetry and Remote Sensing, 129, 212-225.
https://doi.org/10.1016/j.isprsjprs.2017.05.001

has been cited by the following article:

• TITLE: Classification of Blood Species Using Fluorescence Spectroscopy Combined with Deep Learning Method

  AUTHORS: Jianhong Gan, Linhua Zhou, Jian Cui, Boqi Man, Xiaoning Jia, Sanzhi Shi, Linna Liu

  KEYWORDS: Neural Network Model, Deep Learning, Classification, Blood Species, Fluorescence Spectroscopy

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.7 No.10, October 15, 2019

  ABSTRACT: In this work, a deep belief neural network model (DBN) was developed to classify doves, chickens, mice and sheep blood samples, which have many similarities in composition causing their spectra to look almost identical by visual comparison alone. The DBN model was formulated for the feature extraction from the pretreated fluorescence spectroscopy. Then, cross-validation results showed that the application of deep learning method made it possible to classify the blood fluorescence spectroscopy in a more precise way than previous methods. Especially, the classification accuracy of whole blood with 1% of concentration was up to 97.5%.