Animal Detection Using Thermal Images and Its Required Observation Conditions

Information about changes in the population sizes of wild animals is extremely important for conservation and management. Wild animal populations have been estimated using statistical methods, but it is difficult to apply such methods to large areas. To address this problem, we have developed severa...

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Main Authors: Yu Oishi, Hiroyuki Oguma, Ayako Tamura, Ryosuke Nakamura, Tsuneo Matsunaga
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/10/7/1050
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spelling doaj-49f020344a704793af1e1762f8a6279f2020-11-25T00:41:47ZengMDPI AGRemote Sensing2072-42922018-07-01107105010.3390/rs10071050rs10071050Animal Detection Using Thermal Images and Its Required Observation ConditionsYu Oishi0Hiroyuki Oguma1Ayako Tamura2Ryosuke Nakamura3Tsuneo Matsunaga4Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto, Tokyo 135-0064, JapanCenter for Environmental Biology and Ecosystem Studies, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Ibaraki, JapanResearch and Survey Department, Nakanihon Air Service Co., Ltd., 17-1 Wakamiya, Nishikasugai 480-0202, Aichi, JapanArtificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto, Tokyo 135-0064, JapanCenter for Global Environmental Research, National institute for Environmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Ibaraki, JapanInformation about changes in the population sizes of wild animals is extremely important for conservation and management. Wild animal populations have been estimated using statistical methods, but it is difficult to apply such methods to large areas. To address this problem, we have developed several support systems for the automated detection of wild animals in remote sensing images. In this study, we applied one of the developed algorithms, the computer-aided detection of moving wild animals (DWA) algorithm, to thermal remote sensing images. We also performed several analyses to confirm that the DWA algorithm is useful for thermal images and to clarify the optimal conditions for obtaining thermal images (during predawn hours and on overcast days). We developed a method based on the algorithm to extract moving wild animals from thermal remote sensing images. Then, accuracy was evaluated by applying the method to airborne thermal images in a wide area. We found that the producer’s accuracy of the method was approximately 77.3% and the user’s accuracy of the method was approximately 29.3%. This means that the proposed method can reduce the person-hours required to survey moving wild animals from large numbers of thermal remote sensing images. Furthermore, we confirmed the extracted sika deer candidates in a pair of images and found 24 moving objects that were not identified by visual inspection by an expert. Therefore, the proposed method can also reduce oversight when identifying moving wild animals. The detection accuracy is expected to increase by setting suitable observation conditions for surveying moving wild animals. Accordingly, we also discuss the required observation conditions. The discussions about the required observation conditions would be extremely useful for people monitoring animal population changes using thermal remote sensing images.http://www.mdpi.com/2072-4292/10/7/1050movementobservation conditionssurveythermal imagewild animal
collection DOAJ
language English
format Article
sources DOAJ
author Yu Oishi
Hiroyuki Oguma
Ayako Tamura
Ryosuke Nakamura
Tsuneo Matsunaga
spellingShingle Yu Oishi
Hiroyuki Oguma
Ayako Tamura
Ryosuke Nakamura
Tsuneo Matsunaga
Animal Detection Using Thermal Images and Its Required Observation Conditions
Remote Sensing
movement
observation conditions
survey
thermal image
wild animal
author_facet Yu Oishi
Hiroyuki Oguma
Ayako Tamura
Ryosuke Nakamura
Tsuneo Matsunaga
author_sort Yu Oishi
title Animal Detection Using Thermal Images and Its Required Observation Conditions
title_short Animal Detection Using Thermal Images and Its Required Observation Conditions
title_full Animal Detection Using Thermal Images and Its Required Observation Conditions
title_fullStr Animal Detection Using Thermal Images and Its Required Observation Conditions
title_full_unstemmed Animal Detection Using Thermal Images and Its Required Observation Conditions
title_sort animal detection using thermal images and its required observation conditions
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2018-07-01
description Information about changes in the population sizes of wild animals is extremely important for conservation and management. Wild animal populations have been estimated using statistical methods, but it is difficult to apply such methods to large areas. To address this problem, we have developed several support systems for the automated detection of wild animals in remote sensing images. In this study, we applied one of the developed algorithms, the computer-aided detection of moving wild animals (DWA) algorithm, to thermal remote sensing images. We also performed several analyses to confirm that the DWA algorithm is useful for thermal images and to clarify the optimal conditions for obtaining thermal images (during predawn hours and on overcast days). We developed a method based on the algorithm to extract moving wild animals from thermal remote sensing images. Then, accuracy was evaluated by applying the method to airborne thermal images in a wide area. We found that the producer’s accuracy of the method was approximately 77.3% and the user’s accuracy of the method was approximately 29.3%. This means that the proposed method can reduce the person-hours required to survey moving wild animals from large numbers of thermal remote sensing images. Furthermore, we confirmed the extracted sika deer candidates in a pair of images and found 24 moving objects that were not identified by visual inspection by an expert. Therefore, the proposed method can also reduce oversight when identifying moving wild animals. The detection accuracy is expected to increase by setting suitable observation conditions for surveying moving wild animals. Accordingly, we also discuss the required observation conditions. The discussions about the required observation conditions would be extremely useful for people monitoring animal population changes using thermal remote sensing images.
topic movement
observation conditions
survey
thermal image
wild animal
url http://www.mdpi.com/2072-4292/10/7/1050
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