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...
Main Authors: | , , , , |
---|---|
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 |
id |
doaj-49f020344a704793af1e1762f8a6279f |
---|---|
record_format |
Article |
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 |
work_keys_str_mv |
AT yuoishi animaldetectionusingthermalimagesanditsrequiredobservationconditions AT hiroyukioguma animaldetectionusingthermalimagesanditsrequiredobservationconditions AT ayakotamura animaldetectionusingthermalimagesanditsrequiredobservationconditions AT ryosukenakamura animaldetectionusingthermalimagesanditsrequiredobservationconditions AT tsuneomatsunaga animaldetectionusingthermalimagesanditsrequiredobservationconditions |
_version_ |
1725285696355172352 |