NDLI: Siamese hyperspectral target detection using synthetic training data

Content Provider	IET Digital Library
Author	Kim, J. H. Joung, J.
Abstract	This Letter presents a method for training convolutional neural networks (CNNs) that detect targets of interest in hyperspectral images. Collecting suitable and abundant training data has been the main obstacle to the successful application of CNNs to hyperspectral target detection. To solve the problem, the authors propose a scheme to generate synthetic training data. Publicly available spectral reflectance library and an easy-to-obtain radiative transfer model are utilised in their scheme. Using the synthetic training data only, a Siamese CNN is trained to learn robust features for the detection task. Experimental results on the real hyperspectral image show the effectiveness of the proposed method.
Starting Page	1116
Ending Page	1118
Page Count	3
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 21, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/21
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.1758
Journal	Electronics Letters
Publisher Date	2020-09-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Abundant Training Data CNNs Computer Vision And Image Processing Technique Convolutional Neural Network Feature Extraction Geophysical Image Processing Hyperspectral Image Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Knowledge Engineering Technique Learning in AI Neural Computing Technique Neural Nets Object Detection Optical, Image And Video Signal Processing Radiative Transfer Siamese Hyperspectral Target Detection Suitable Training Data Synthetic Training Data
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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