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UAV画像を用いた水面の水鳥の自動カウント
http://hdl.handle.net/10659/00006626
http://hdl.handle.net/10659/00006626c20e057b-d96f-4894-9bf5-622493596a62
名前 / ファイル | ライセンス | アクション |
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R-2020-46_ogawa.pdf (6.5 MB)
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2020-11-18 | |||||
タイトル | ||||||
タイトル | UAV画像を用いた水面の水鳥の自動カウント | |||||
言語 | ||||||
言語 | jpn | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | UAV (Unmanned Aerial Vehicle; drone) | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Lake Miyajima-numa | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Greather white-fronted goose (Anser albifrons) | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Machine leaning | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | UAV (Unmanned Aerial Vehicle; drone) | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | Lake Miyajima-numa | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | Greather white-fronted goose (Anser albifrons) | |||||
キーワード | ||||||
言語 | en | |||||
主題Scheme | Other | |||||
主題 | Machine leaning | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
小川, 健太
× 小川, 健太× 牛山, 克巳× 小練, 史弥 |
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著者別名 | ||||||
姓名 | 小川, 健太 | |||||
著者別名 | ||||||
姓名 | USHIYAMA, Katsumi | |||||
著者別名 | ||||||
姓名 | KONERI, Fumiya | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | The monitoring of migratory geese at known stopover sites is crucial to their habitat conservation but usually requires skilled manpower for counting large flocks of waterfowl. The use of observations from UAVs (unmanned aerial vehicles, a.k.a. drones) is a potential alternative to traditional bird counting methods. We used a multicopter-type UAV with a well-stabilized camera to count greater white-fronted geese (Anser albifrons) that seasonally roost in Lake Miyajima-numa, Hokkaido, Japan. Since the geese roost at sundown, we had to find good camera settings that enabled the detection of geese on the lake under dim light conditions. The key camera setting was a very long explosion time of half a second, which allowed us to detect and count geese up to about 30 minutes after sunset. A single UAV flight could observe the entire lake from an altitude of 100 m above the water surface with little disturbance to the roosting geese. We used a cascade classifier, which is a machine leaning technique, to automatically count geese in the imagery. The counting accuracy ranged from -4.1 % to+6.1 % in four validation cases compared with manual counts on the UAV image. We conclude that the combination of UAV and machine leaning methods can yield goose counts with an accuracy of ±15 %. The results suggest that this approach will be useful for monitoring geese or other waterfowl. | |||||
書誌情報 |
日本リモートセンシング学会誌 巻 39, 号 5, p. 363-370, 発行日 2019-11 |
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ISBN | ||||||
識別子タイプ | ISBN | |||||
関連識別子 | 0289-7911 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.11440/rssj.39.363 | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
その他のタイトル | ||||||
その他のタイトル | Automated Counting of Waterfowl on Water Surface Using UAV Imagery | |||||
出版者 | ||||||
出版者 | 日本リモートセンシング学会 | |||||
資源タイプ | ||||||
内容記述タイプ | Other | |||||
内容記述 | Article |