Cargando…
Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment
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...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215119/ https://www.ncbi.nlm.nih.gov/pubmed/30424428 http://dx.doi.org/10.3390/mi9100495 |
_version_ | 1783368079871311872 |
---|---|
author | Kim, Sungho Kim, Jungho Lee, Jinyong Ahn, Junmo |
author_facet | Kim, Sungho Kim, Jungho Lee, Jinyong Ahn, Junmo |
author_sort | Kim, Sungho |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6215119 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62151192018-11-06 Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment Kim, Sungho Kim, Jungho Lee, Jinyong Ahn, Junmo Micromachines (Basel) Article 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. MDPI 2018-09-27 /pmc/articles/PMC6215119/ /pubmed/30424428 http://dx.doi.org/10.3390/mi9100495 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kim, Sungho Kim, Jungho Lee, Jinyong Ahn, Junmo Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment |
title | Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment |
title_full | Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment |
title_fullStr | Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment |
title_full_unstemmed | Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment |
title_short | Midwave FTIR-Based Remote Surface Temperature Estimation Using a Deep Convolutional Neural Network in a Dynamic Weather Environment |
title_sort | midwave ftir-based remote surface temperature estimation using a deep convolutional neural network in a dynamic weather environment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6215119/ https://www.ncbi.nlm.nih.gov/pubmed/30424428 http://dx.doi.org/10.3390/mi9100495 |
work_keys_str_mv | AT kimsungho midwaveftirbasedremotesurfacetemperatureestimationusingadeepconvolutionalneuralnetworkinadynamicweatherenvironment AT kimjungho midwaveftirbasedremotesurfacetemperatureestimationusingadeepconvolutionalneuralnetworkinadynamicweatherenvironment AT leejinyong midwaveftirbasedremotesurfacetemperatureestimationusingadeepconvolutionalneuralnetworkinadynamicweatherenvironment AT ahnjunmo midwaveftirbasedremotesurfacetemperatureestimationusingadeepconvolutionalneuralnetworkinadynamicweatherenvironment |