Next Article in Journal
Post-Release Deformation and Motion Control of Photonic Waveguide Beams by Tuneable Electrothermal Actuators in Thick SiO2
Next Article in Special Issue
A Trace C2H2 Sensor Based on an Absorption Spectrum Technique Using a Mid-Infrared Interband Cascade Laser
Previous Article in Journal
Design Choices for Next-Generation Neurotechnology Can Impact Motion Artifact in Electrophysiological and Fast-Scan Cyclic Voltammetry Measurements
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle

Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment

1
Department of Electronic Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea
2
Agency for Defense Development, 111 Sunam-dong, Daejeon 34186, Korea
*
Author to whom correspondence should be addressed.
Micromachines 2018, 9(10), 495; https://doi.org/10.3390/mi9100495
Received: 4 September 2018 / Revised: 26 September 2018 / Accepted: 26 September 2018 / Published: 27 September 2018
(This article belongs to the Special Issue Infrared Nanophotonics: Materials, Devices, and Applications)
  |  
PDF [1875 KB, uploaded 27 September 2018]
  |  

Abstract

Remote measurements of thermal radiation are very important for analyzing the solar effect in various environments. This paper presents a novel real-time remote temperature estimation method by applying a deep learning-based regression method to midwave infrared hyperspectral images. A conventional remote temperature estimation using only one channel or multiple channels cannot provide a reliable temperature in dynamic weather environments because of the unknown atmospheric transmissivities. This paper solves the issue (real-time remote temperature measurement with high accuracy) with the proposed surface temperature-deep convolutional neural network (ST-DCNN) and a hyperspectral thermal camera (TELOPS HYPER-CAM MWE). The 27-layer ST-DCNN regressor can learn and predict the underlying temperatures from 75 spectral channels. Midwave infrared hyperspectral image data of a remote object were acquired three times a day (10:00, 13:00, 15:00) for 7 months to consider the dynamic weather variations. The experimental results validate the feasibility of the novel remote temperature estimation method in real-world dynamic environments. In addition, the thermal stealth properties of two types of paint were demonstrated by the proposed ST-DCNN as a real-world application. View Full-Text
Keywords: midwave infrared; thermal radiation; hyperspectral; remote surface temperature; weather variation; deep learning; regressor; thermal stealth midwave infrared; thermal radiation; hyperspectral; remote surface temperature; weather variation; deep learning; regressor; thermal stealth
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Kim, S.; Kim, J.; Lee, J.; Ahn, J. Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment. Micromachines 2018, 9, 495.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Micromachines EISSN 2072-666X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top