19沼津工業高等専門学校研究報告
第 53 号，2019 年 1 月㟼ᒸ┴ᮾ㒊࡟࠾ࡅࡿᶵᲔᏛ⩦ࢆ⏝࠸ࡓࣄࣀ࢟ᯘࡢ᥎ᐃ̿✵୰෗┿ࢆ⏝࠸ࡓணഛⓗㄪᰝ̿ᑠ⠛෽኱ ㄶゼᑦஓ Ᏻ⏣Ὀ㍜ 㕥ᮌ㟼⏨ Estimation of Cypress tree occupancy in forests by machine learning in the easternarea of Shizuoka Prefecture: A preliminary investigation by aerial photographs
Ryota KOSHINO*1, Naoya SUWA*1, Taisuke YASUDA*2, Shizuo SUZUKI*1
Abstract
Aerial photographs offer broad spatial coverage of generalized forest structure and promise toimprove the measurement and mapping of forest properties. The goal of this paper was toinvestigate the effectiveness of object scale (12 u 12 pixels) prediction of a species occupancy onobject-based image analysis using aerial photographs. The study was carried out on a mountainareas located in Numazu, Kawazu and Izu districts, in the eastern part of Shizuoka Prefecture.
The object-based image analysis was conducted using RGR and HSV color spaces to classify theCypress forests which cover the largest part of Shizuoka Prefecture. The random forests
ensemble learning approach was used to predict the object scale probability of occurrence forthe conifer species. The area under a curve (AUC) of a receiver operating characteristic (ROC)
was used to judge classification accuracy. The AUC was improved by increasing the number oftraining samples in the machine learning and it saturated when training samples exceeded
15,000. The fluctuations of false positive rate, false negative rate and misclassification rate
were reduced as the number of decision trees increased, and they became stable for more than
1,000 decision trees. Under the condition of 24,000 training samples and 2,000 decision trees,
the classification exhibited high accuracy of over 0.83 with the AUC. The classifications using
RGB and HSV color spaces achieved similar accuracies and provided a more visually adequate
depiction of the Cypress forests. It was concluded that the random forests ensemble learning
approach is a powerful machine learning classifier when combined with object-based imageanalysis using aerial photographs.
Key Words: Aerial photographs, Object-based image analysis, Random forests, Remote sensing඲ᅜࡢ⣙ 40㸣ࡢ᳜⏕ᅗࡢ᭦᪂ࡀᮍ᏶࡛࠶ࡾ[1]ࠊ1999 ᖺࡣࡌࡵ࡟௨㝆ࠊ⌧Ꮡࡍࡿ᳜⏕ᅗ඲యࡢ᭦᪂ࡣ 20 ᖺ㛫⾜ࢃࢀ࡚࠸⎔ቃ┬ࡣ⮬↛⎔ቃಖ඲ᇶ♏ㄪᰝࢆ⾜࠸ࠊ඲ᅜࡢ᳜⏕ᅗࡢᩚഛࡸ᭦᪂ࢆ⾜ࡗ࡚࠸ࡿࠋ➨ 6 ᅇ⮬↛⎔ቃಖ඲ㄪᰝ࡛࡞࠸[2]ࠋࡣࠊ᳜⏕ᅗࡢ⦰ᑻࡀ 1/50,000 ⦰ᑻ࠿ࡽ 1/25,000 ⦰ᑻ࡜㏆ᖺࠊᆅ⌫ᬮ໬࡟క࠸⎔ቃࡢኚ໬ࡀᛴ㏿࡟࡞ࡗ࡚࠸࡞ࡗࡓࠋ⦰ᑻࡢኚ᭦࠿ࡽసᴗ㔞ࡀቑຍࡋࠊ2013 ᖺ᫬Ⅼ࡛ࡿࠋ᳜⏕᝟ሗࡢ⟶⌮ࡸ⏕ែ⣔ࡢ⌧≧ᢕᥱࡢࡓࡵ▷࠸࿘ᮇ*1 㟁ᏊไᚚᕤᏛ⛉࡛ࡢᐃᮇⓗ࡞᳜⏕ᅗࡢ᭦᪂ࡀᚲせ࡛࠶ࡿࠋࡋ࠿ࡋࠊୖ㏙

Department of Electronic Control System Engineeringࡋࡓసᴗ㔞ࡢቑຍࡸ⌧ሙసᴗ⪅ࡢ㧗㱋໬࡟ࡼࡾ᳃ᯘࡢ᝟

*2 ᒣ᲍┴ᐩኈᒣ⛉Ꮫ◊✲ᡤሗ⟶⌮ࡀᅔ㞴࡟࡞ࡗ࡚ࡁ࡚࠸ࡿࠋᚑ᮶ࡢసᡂ᪉ἲࡣࠊ✵Mount Fuji Research Institute, Yamanashi୰෗┿ࢆ┠ど࡛ㄞࡳྲྀࡾࠊᆅᅗ࡟ቃ⏺࡜ซ౛ࢆධຊࡋࡓ

Prefectural Government.

ண1ᅗࢆసᡂࡍࡿࠋḟ࡟⌧ᆅㄪᰝࢆ⾜࠸ࠊணᅗࢆಟṇ

20

沼津工業高等専門学校研究報告

第 53 号

ࡍࡿࠋࡇࢀࡽࡢసᴗࢆᶵᲔᏛ⩦࡟ࡼࡗ࡚⾜࠺ࡇ࡜ࡣࠊ᳜ࡓ ࣞ ࢖ ࣖ ᝟ ሗ ࢹ ࣮ ࢱ GISMaps.tsv ࡜ ࣉ ࣛ ࢢ ࢖ ࣥ⏕ᅗࡢసᡂ㢖ᗘࢆୖࡆࡿࡇ࡜࡟ࡘ࡞ࡀࡿ࡜⪃࠼ࡽࢀ࡚࠸

TileLayerPlugin ࢆ⏝࠸ࡓࠋࡿ[3]ࠋࡑࡇ࡛ࠊ✵୰෗┿ࡸ⾨ᫍ⏬ീࢆ⏝࠸ࡓ୍ᣓࡋࡓ᳃ᯘ᳜⏕ᅗࡢసᡂࡸ᳜⏕≧ἣࡢᢕᥱࡀᮃࡲࢀࡿࠋᅜ᭷ᯘཬࡧẸ᭷ᯘࢹ࣮ࢱศゎ⬟ࡀ㧗࠸ࢹ࣮ࢱࡢ⏬ീศ㢮ࢆ⾜࠺㝿ࠊศゎ⬟ࡢ㧗ࡉ࠿ࡽ⏬⣲㸦ࣆࢡࢭࣝ㸧࣮࣋ࢫࡢ⏬ീศ㢮ἲࢆ⏝࠸ࡓሙྜࠊྠࡌ᳜≀࡛࠶ࡗ࡚ࡶᙳ࡞࡝࡟ࡼࡿᚤᑠ㡿ᇦࡀ኱㔞࡟Ⓨ⏕ࡋ࡚ࡋࡲ࠺[4]ࠋࡑࡢࡓࡵࣆࢡࢭࣝྠኈࡢ✵㛫ⓗ㛵ಀᛶࢆ㔜どࡋࠊᙧ≧ࡸ࿘ᅖ࡜ࡢ㛵ಀ࡞࡝ࢆ⪃៖ࡋࡓ࢜ࣈࢪ࢙ࢡࢺ࣮࣋ࢫࡢศ㢮ἲࡀ᭷⏝࡛࠶ࡿ࡜ࡉࢀ࡚࠸ࡿࠋ࢜ࣈࢪ࢙ࢡࢺ࣮࣋ࢫࡢศ㢮ἲ࡛ࡣࠊࣆࢡࢭࣝࡢ㞟ྜయ࡛࠶ࡿ࢜ࣈࢪ࢙ࢡࢺࢆ⏬ീศ㢮ࡢ༢఩࡜ࡋ࡚࠸ࡿ[4]ࠋ࢜ࣈࢪ࢙ࢡࢺࢆ⏝࠸ࡿࡇ࡜࡛ࠊࢸࢡࢫࢳࣕࡢ≉ᚩࢆ⾲⌧ࡋ⏬ീศ

Fig.2 ᯘ⌜ቃ⏺

㢮࡟ᛂ⏝ࡍࡿࡇ࡜ࡀᐜ᫆࡟࡞ࡿࠋࡲࡓࠊ᳜⏕ᅗࡢቃ⏺⥺ᅜ᭷ᯘ㔝᪋ᴗᐇ᪋ィ⏬ᅗ[8]ࢆᇶ࡟సᡂࡋࡓࡶࡢ࡛࠶ࡾࠊ࡜ࡋ࡚࢜ࣈࢪ࢙ࢡࢺࡢቃ⏺⥺ࡀ㐺⏝࡛ࡁࡿ࡜⪃࠼ࡽࢀࠊ⃰ࡃࡠࡾࡘࡪࡋࡓ㒊ศࡣࣄࣀ࢟ࢆ♧ࡍ᳜⏕ᅗ໬࡟᭷⏝࡛࠶ࡿ࡜ᮇᚅࡉࢀ࡚࠸ࡿ[5]ࠋ㟼ᒸ┴ࡣ┴ᅵࡢ 㸣ࡀ᳃ᯘ࡛࠶ࡾࠊࣄࣀ࢟ᯘࡢ㠃✚ࡣIᅜ᭷ᯘࠊẸ᭷ᯘ࡜ࡶ࡟᭱ࡶᗈ࠸[6]ࠋ㟼ᒸ┴඲ᇦ࡟࠾࠸࡚ࣄࣀ࢟ᯘࡢศ㢮ࢆ⾜࠺ࡓࡵࡢணഛⓗ࡞ㄪᰝ࡜ࡋ࡚ࠊ✵୰෗┿ࢆ⏝࠸ࡓ⏬ീศ㢮ࢆᶵᲔᏛ⩦࡟ࡼࡗ࡚ヨࡳࡓࠋII㻌III㻌᪉ἲ
Fig.1 ࡟◊✲ࡢὶࢀࢆ♧ࡍࠋ஌௔੦ম௕㻌૬র୼ઌIV㻌ઇపॹ‫ॱش‬㻌

Fig.3 㟼ᒸ┴ᮾ㒊ཬࡧఀ㇋༙ᓥ࡟࠾ࡅࡿᑐ㇟㡿ᇦ઺൸ীథ㻌ਫ਼઒ॹ‫ॱش‬㻌ᅜ᭷ᯘࡢࢹ࣮ࢱࡣࠊᅜ᭷ᯘ㔝᪋ᴗᐇ᪋ィ⏬ᅗ[8]ࢆධᡭ
ࡋࠊࢫ࢟ࣕࢼ࣮࡟ࡼࡾ⏬ീࢹ࣮ࢱ࡟ኚ᥮ࡋࡓࠋḟ࡟ࢫ࢟ࣕࣥࡋࡓ⏬ീࢆࡶ࡜࡟ᯘ⌜ቃ⏺ࢆ QGIS ࢆ⏝࠸࡚ᡭධຊ௬੼㻌࡛సᡂࡋࠊshape ࣇ࢓࢖ࣝᙧᘧ࡟ࡋࡓ㸦Fig.2㸧ࠋࡇࡇ࡛Fig.1◊✲ࡢὶࢀᯘ⌜࡜ࡣ᳃ᯘࢆᏐ⏺ࡸᑿ᰿ࠊ㇂➼ࡢᆅᙧ࡛ศࡅࡓ༊⏬࡛࠶ࡾࠊᶞ✀ࡢ᝟ሗࡀྵࡲࢀࡿࠋẸ᭷ᯘࡢࢹ࣮ࢱࡣࠊshapeࢹࢪࢱࣝࢹ࣮ࢱ

ࣇ࢓࢖ࣝᙧᘧ࡛ධᡭࡋࡓ[9]ࠋࡇࡢࣇ࢓࢖ࣝ࡟ࡣࠊ᳃ᯘィ✵୰෗┿ࢹ࣮ࢱ⏬ᅗ࡜᳃ᯘ⡙ࡀྵࡲࢀࡿࠋᮏ◊✲࡛౑⏝ࡍࡿ✵୰෗┿ࡣࠊᅜᅵᆅ⌮㝔ࡀබ㛤ࡋ࡚࠸ࡿ㟁Ꮚᅜᅵᇶᮏᅗ㸦࣮࢝ࣛ࢜ࣝࢯ⏬ീ㸧[7]㸦2010 ᖺ 4ᩍᖌཬࡧ᳨ドࢹ࣮ࢱ⠊ᅖ

᭶ཬࡧ 2011 ᖺ 5 ᭶㸧ࡢ 1/10,000 ⦰ᑻࡢࡶࡢࢆ JPG ᙧᑐ㇟㡿ᇦࡣ Fig.3 ࡟♧ࡍ㟼ᒸ┴἟ὠᕷ(I, II)ࠊఀ㇋ᕷ

ᘧ࡛ධᡭࡋࡓࠋ✵୰෗┿ࡢᆅୖศゎ⬟ࡣ⣙ 0.5 m ࡛࠶ࡿࠋ(III)ཬࡧἙὠ⏫(IV)࡜ࡋࡓࠋ἟ὠ㡿ᇦ࡟ࡣࣄࣀ࢟࡜ᗈⴥ✵୰෗┿ࢆᆅ⌮᝟ሗࢩࢫࢸ࣒ࢯࣇࢺ࢙࢘࢔ QGIS (2.18ᶞࡀከࡃ⏕⫱ࡋࠊఀ㇋ཬࡧἙὠ㡿ᇦ࡛ࡣࣄࣀ࢟࡜ࢫࢠࡀ

with Grass)࡟ㄞࡳ㎸ࡴࡓࡵ࡟ࠊGitHubGist ࡼࡾධᡭࡋከࡃ⏕⫱ࡍࡿࠋࡲࡓࠊFig.4 ࡣఀ㇋㡿ᇦࢆ 4 ࡘ࡟ศ๭ࡋ2

静岡県東部における機械学習を用いたヒノキ林の推定

21―空中写真を用いた予備的調査―ࡓࡶࡢ࡛࠶ࡿࠋࡑࢀࡒࢀࡢᑐ㇟㡿ᇦ࡛ձ,մࢆᩍᖌࢹ࣮ࢱ࡛࠶ࡿࠋᶵᲔᏛ⩦࡟⏝࠸ࡿ⤫ィ㔞࡜ࡋ࡚ࠊ࢜ࣈࢪ࢙ࢡࢺ

⠊ᅖࠊղ,ճࢆ᳨ドࢹ࣮ࢱ⠊ᅖ࡜ࡋࡓࠋ

୰࡟ྵࡴྛ⏬⣲ࡢ RGB ್ࡲࡓࡣ HSV ್ࡢᖹᆒཬࡧᶆ‽೫ᕪࢆ⏝࠸ࡓࠋRGB Ⰽ✵㛫࡛ࡢ⏬ീศ㢮࡛ࡣྛ࢜ࣈࢪ࢙ࢡࢺ࡟࠾ࡅࡿ⏬⣲ࡢ㉥ࠊ⥳ཬࡧ㟷ࡢྛᖹᆒཬࡧᶆ‽೫ᕪࡢ 6 ಶࡢ⤫ィ㔞ࢆ౑⏝ࡋࡓࠋࡲࡓࠊHSV Ⰽ✵㛫࡛ࡣⰍ┦ࠊᙬᗘཬࡧ᫂ᗘࡢྛᖹᆒཬࡧᶆ‽೫ᕪࡢ 6 ಶࢆ౑⏝ࡋࡓࠋྛỴᐃᮌࡢศᒱⅬ࡟࠾ࡅࡿศᒱ㛵ᩘೃ⿵ࡢᩘࡣࠊ౑⏝ࡍࡿ⤫ィ㔞ࡢᖹ᪉᰿ᩘࡀ᭱㐺࡛࠶ࡿ࡜࠸࠺◊✲ሗ࿌ࡀ࠶ࡿ[12]ࠋࡇࡢࡓࡵࠊ6 ಶࡢ⤫ィ㔞࠿ࡽ 2 ಶࢆศᒱ㛵ᩘೃ⿵࡜ࡋ࡚౑⏝ࡋࡓࠋ1 ࡘࡢ࢜ࣈࢪ࢙ࢡࢺ࡟ᑐࡋ࡚ࠊྛỴᐃᮌࡀ஧್(0 ࡲࡓࡣ 1)ࡢุ᩿ࢆ⾜࠸ࠊỴᐃᮌᩘࡢከᩘỴ࡟Fig.4 ᩍᖌཬࡧ᳨ドࢹ࣮ࢱ⠊ᅖࡢศ๭౛ࡼࡿ๭ྜࡀồࡲࡿࠋ0 ࠿ࡽ 1 ࡲ࡛ࡢ㐃⥆ⓗ࡞್ࢆฟຊࡋࠊࡇࡢ್ࢆ➉ᯘศᕸண⏬ീࡢฎ⌮

Python (3.6)ࡢ OpenCV (2.4)ࣛ࢖ࣈࣛࣜࢆ⏝࠸࡚✵୰್࡜ᐃ⩏ࡋࡓࠋ⢭ᗘホ౯෗┿ࡢ RGB㸦㉥: Red, ⥳: Green, 㟷: Blue㸧Ⰽ✵㛫࠿ࡽ⏬ീศ㢮ࡢ⢭ᗘࢆホ౯ࡍࡿࡓࡵ࡟ Receiver operating

HSV㸦Hue: Ⰽ┦, Saturation: ᙬᗘ, Value: ᫂ᗘ㸧Ⰽ✵characteristic (ROC) ᭤⥺࡜࿧ࡤࢀࡿᡭἲࢆ⏝࠸ࡓࠋᶓ㛫࡬ኚ᥮ࡋࡓࠋ⏬ീศ㢮ࡣࠊRGB Ⰽ✵㛫ࡔࡅ࡛࡞ࡃࠊ

㍈࡟ഇ㝧ᛶ⋡ࠊ⦪㍈࡟┿㝧ᛶ⋡ࢆ࡜ࡾࠊ㝧ᛶ࠿㝜ᛶ࠿ࢆ

HSV Ⰽ✵㛫ࡶ⏝࠸ࠊ୧⪅ࡢศ㢮⢭ᗘࢆẚ㍑ࡋࡓࠋศࡅࡿ㜈್ࢆኚ໬ࡉࡏ࡚᭤⥺ࡀᥥ࠿ࢀࠊ୺࡟⏬ീศ㢮ࡢ⢭ᗘホ౯࡟⏝࠸ࡽࢀࡿࠋࣄࣀ࢟ࡢ࢜ࣈࢪ࢙ࢡࢺࢆࠕࣄࣀ࡛࢟࠶ࡿࠖࠊࠕࣄࣀ࡛࢟ࢸࢡࢫࢳࣕゎᯒ࡞ ࠸ ࠖ ࡜ ศ 㢮 ࡋ ࡓ ࢜ ࣈ ࢪ ࢙ ࢡ ࢺ ᩘ ࢆ ࡑ ࢀ ࡒ ࢀ true
positive (TP)ࠊfalse negative (FN)ࠊࣄࣀ࡛࢟࡞࠸࢜ࣈ
ࢪ࢙ࢡࢺࢆࠕࣄࣀ࡛࢟࡞࠸ࠖࠊࠕࣄࣀ࡛࢟࠶ࡿࠖ࡜ศ㢮ࡋࡓ࢜ࣈࢪ࢙ࢡࢺᩘࢆࡑࢀࡒࢀ true negative (TN)ࠊfalse
positive (FP)࡜ᐃ⩏ࡍࡿ࡜ࠊഇ㝧ᛶ⋡࡜┿㝧ᛶ⋡ࡣ௨ୗࡢࡼ࠺࡟⾲ࡏࡿࠋி௉ഇ㝧ᛶ⋡ ൌ ி௉ା்ே ,்௉┿㝧ᛶ⋡ ൌ ்௉ାிே

Fig.5 ࢸࢡࢫࢳࣕゎᯒ⏝ࡢṇ᪉ᙧ࢜ࣈࢪ࢙ࢡࢺ

ROC ᭤⥺ࡣ᭤⥺ୗࡢ㠃✚࡛࠶ࡿ Area under a curveኴ࠸⥺࡛༊ษࡽࢀࡓ 1 ༊⏬ࡀ 1 ࢜ࣈࢪ࢙ࢡࢺ࡟┦ᙜࡋ(AUC)࡛ᐃ㔞ⓗ࡟ホ౯࡛ࡁࠊAUC ࡣࠊ0 ࠿ࡽ 1 ࡢ್ࢆ㐃1212 ࣆࢡࢭࣝ࠿ࡽᡂࡿ

⥆ⓗ࡟࡜ࡿࠋ᏶඲࡞ศ㢮ࡣ 1ࠊ඲ࡃศ㢮ࡀฟ᮶࡚࠸࡞࠸ࣛࣥࢲ࣒࡞ศ㢮ࡣ 0.5 ࡜࡞ࡿࠋຍ࠼࡚ࠊഇ㝜ᛶ⋡࡜ㄗ⟅Ἑ㑚ࡽ [10] ࡣᑐ㇟࡜࡞ࡿ⏬ീ࡟ࣆࢡࢭࣝࡢ㞟ྜయ࡛࠶⋡ࢆ௨ୗࡢࡼ࠺࡟⾲ࡍࠋࡿṇ᪉ᙧࡢ࢜ࣈࢪ࢙ࢡࢺࢆసᡂࡋࠊࡑࡢ࢜ࣈࢪ࢙ࢡࢺෆிே

ഇ㝜ᛶ⋡ ൌ ்௉ାிே ,ࡢ RGB ್ࡢᖹᆒ࡜ᶆ‽೫ᕪࢆ⏝࠸࡚ࢸࢡࢫࢳࣕゎᯒࢆ⾜࠸Ⰻዲ࡞⤖ᯝࢆᚓࡓࠋࡑࡇ࡛ᮏ◊✲࡛ࡶṇ᪉ᙧࡢ࢜ࣈࢪ࢙ࢡࢺࢆタᐃࡋࠊࢸࢡࢫࢳࣕゎᯒࢆ⾜ࡗࡓࠋἙ㑚ࡽ[10]⤖ᯝཬࡧ⪃ᐹ࡜ྠᵝ࡟ࠊᶞᮌࡀ 1ࠊ2 ᮏධࡿࡼ࠺࡟࢜ࣈࢪ࢙ࢡࢺࡢ኱ࣃ࣓࣮ࣛࢱࡢỴᐃ

ࡁࡉࢆ 1212 ࣆࢡࢭࣝࠊィ 144 ࣆࢡࢭࣝ࡜ࡋࡓ(Fig. 5)ࠋ

ி௉ାிேㄗ⟅⋡ ൌ ்௉ାி௉ା்ேାிே⏬ീศ㢮࡟⏝࠸ࡓࣛࣥࢲ࣒ࣇ࢛ࣞࢫࢺࡢ⢭ᗘ࡟ᙳ㡪ࡍࡿせᅉ࡜ࡋ࡚ᩍᖌࢹ࣮ࢱᩘ࡜Ỵᐃᮌᩘࡀᣲࡆࡽࢀࡿࠋࡇ

ᶵᲔᏛ⩦ࢀࡽ࡟ࡘ࠸᳨࡚ウࡋࡓࠋᶵᲔᏛ⩦ࡣࣛࣥࢲ࣒ࣇ࢛ࣞࢫࢺ[11]ࢆ⏝࠸ࡓࠋᐇ⾜⎔ቃᮏ◊✲ࡣࠊRGB ཬࡧ HSV ⏬ീ࡟ᑐࡋ࡚ᩍᖌࢹ࣮ࢱᩘ

ࡣࠊR (3.5)࡜ᶵᲔᏛ⩦⏝ࣃࢵࢣ࣮ࢪ randomForest (4.6)࡟ࡼࡿศ㢮⢭ᗘࡢẚ㍑ࢆ⾜ࡗࡓࠋᩍᖌࢹ࣮ࢱࡀቑ࠼ࡿ࡜3

22

沼津工業高等専門学校研究報告

第 53 号ศ㢮⢭ᗘࢆ⾲ࡍ AUC ࡢ್ࡣୖ᪼ࡋࡓ㸦Fig. 6㸧ࠋࡋ࠿ࡋࠊ

࢙ࢡࢺࡢࢧ࢖ࢬ㸦㸯࢜ࣈࢪ࢙ࢡࢺ࡟ྵࡲࢀࡿࣆࢡࢭࣝᩘ㸧ᩍᖌࢹ࣮ࢱᩘࡀ 15,000 ࠿ࡽ 24,000 ࡟ቑຍࡋ࡚ࡶ AUC

ࡀᑠࡉࡃ࡞ࡿ࡟ࡘࢀ኱ࡁࡃ࡞ࡗࡓ࡜ሗ࿌ࡋ࡚࠸ࡿࠋᮏ◊ࡣࠊ࡯ࡰྠࡌ࡛࠶ࡗࡓࠋ

✲࡛ࡣࠊᏳᐃࡍࡿỴᐃᮌᩘࢆồࡵࡿࡇ࡜ࡀ࡛ࡁࡓࠋࡋ࠿

Feng ࡽ[13]ཬࡧ Ma ࡽ[14]ࡣࠊᑠᆺ↓ேᶵ㸦ࢻ࣮ࣟࣥ㸧ࡋࠊ࢜ࣈࢪ࢙ࢡࢺࡢࢧ࢖ࢬࡣ୍ᐃ࡛࠶ࡿࡓࡵࠊ௒ᚋࡑࡢ

ᦚ㍕ࢹࢪࢱ࣓ࣝ࢝ࣛ࡟ࡼࡾୖ✵࠿ࡽ᧜ᙳࡋࡓ㒔ᕷᬒほࡢࢧ࢖ࢬࢆ᳨ウࡍࡿᚲせࡀ࠶ࡿࠋ⏬ീ࡟ᑐࡋ࡚ᶵᲔᏛ⩦࡟ࡼࡿ᳜⏕ศ㢮ࢆヨࡳࡓࠋ࢜ࣈࢪ࢙ࢡࢺ࣮࣋ࢫࡢࡇࡢศ㢮࡟ࡣࠊ࢔ࣝࢦࣜࢬ࣒࡜ࡋ࡚ࣛࣥศ㢮⢭ᗘࡢホ౯ࢲ࣒ࣇ࢛ࣞࢫࢺࡀ⏝࠸ࡽࢀࠊᩍᖌࢹ࣮ࢱᩘࡀቑຍࡍࡿ࡜

๓㏙ࡢ⤖ᯝ࠿ࡽỴᐃᮌ 2,000 ᮏࠊᩍᖌࢹ࣮ࢱ 24,000ⲡཎࠊᯘࠊ㟢ฟᅵተ➼[13]ࠊ㎰ሙࠊ᳃ᯘࠊᘓ㐀≀ࠊ㐨㊰➼ಶࢆ⏝࠸࡚ࣛࣥࢲ࣒ࣇ࢛ࣞࢫࢺ ࡛ศ㢮ࢆ⾜ࡗࡓ㝿ࡢ[14]ࡢ㆑ู⢭ᗘࡀୖ᪼ࡋࡓ࡜ሗ࿌ࡋ࡚࠸ࡿࠋᮏ◊✲࡛ࡶᩍROC ᭤⥺ࢆసᡂࡋࡓ㸦Fig. 8㸧
ࠋRGB ࡜ HSV ࡢ㛫࡛኱ᖌࢹ࣮ࢱࡢᩘࡀ㔜せ࡛࠶ࡿࡇ࡜ࡀ♧ࡉࢀࡓࠋࡁ࡞㐪࠸ࡣぢࡽࢀ࡞࠿ࡗࡓࠋFig.6 ᩍᖌࢹ࣮ࢱᩘ࡟ࡼࡿศ㢮⢭ᗘ㸦AUC㸧ࡢẚ㍑

Fig.8 Receiver operating characteristic (ROC) ᭤⥺
Table 1 Ⰽ✵㛫ูࡢศ㢮⢭ᗘ࡟㛵ࡍࡿẚ㍑
Ⰽ✵㛫

㜈್

┿㝧ᛶ⋡

ഇ㝧ᛶ⋡

$8&5*%









+69





ᗙᶆ (0, 1) ࡜ ROC ᭤⥺࡜ࡢ㊥㞳ࡀ᭱ᑠ࡜࡞ࡿⅬࡣࠊ⏬ീศ㢮ࡍࡿ㸦㝧ᛶ࠿㝜ᛶ࠿ࢆศࡅࡿ㸧㝿ࡢ㜈್࡜ࡋ࡚㔜せ࡞್࡛࠶ࡿࠋ௒ᅇࡢศ㢮࡟࠾࠸࡚ࠊHSV ཬࡧ RGBࡢ᭱㐺࡞㜈್ࡣࠊ0.5 ࡛࠶ࡗࡓࠋࡇࡢ್࡟ᇶ࡙࠸࡚ Table1 ࡛ࡣࠊⰍ✵㛫ูࡢศ㢮⢭ᗘ࡟㛵ࡋ࡚ẚ㍑ࡋࡓࠋRGB ཬFig.7 Ỵᐃᮌࡢᩘ࡜࢚࣮ࣛ☜⋡ࡢ㛵ಀࡧ HSV ࡛ࡣ┿㝧ᛶ⋡ࠊࡘࡲࡾࣄࣀ࡛࢟࠶ࡿ࡜ࢃ࠿ࡗ࡚࠸ࡿ࢜ࣈࢪ࢙ࢡࢺࢆࣄࣀ࡛࢟࠶ࡿ࡜ண

ᩍᖌࢹ࣮ࢱᩘࡀ༑ศ኱ࡁ࠸ 24,000 ಶࢆ౑⏝ࡋࠊỴᐃࡋࡓ๭ྜࡀࠊᮌᩘ࡜ศ㢮⢭ᗘࡢ㛵ಀࢆㄪ࡭ࡓ㸦Fig. 7㸧ࠋ࢚࣮ࣛ☜⋡࡜

0.71 ཬࡧ 0.73 ࡛࠶ࡗࡓࠋഇ㝧ᛶ⋡ࠊࡘࡲࡾࣄࣀ࡛࢟࡞ࡋ࡚ഇ㝧ᛶ⋡ࠊഇ㝜ᛶ⋡ࠊㄗ⟅⋡ࢆ⏝࠸࡚ホ౯ࡋࡓࠋ5*%࠸࡜ࢃ࠿ࡗ࡚࠸ࡿ࢜ࣈࢪ࢙ࢡࢺࢆࣄࣀ࡛࢟࠶ࡿ࡜ண⏬ീࢆ⏝࠸ࡓ㝿࡟ࠊỴᐃᮌࡢᩘࡀ 1,000 ᮏ௨ୖ࡜࡞ࡿ࡜

ࡓ๭ྜࡀࠊ0.20 ཬࡧ 0.19 ࡛࠶ࡗࡓࠋAUC ࡢ್ࡣ 0.83

ࡋཬࡧ 0.84 ࡛࠶ࡗࡓࠋRGB ⏬ീ࡜ HSV ⏬ീ࡟ࡼࡿศ㢮࢚࣮ࣛ☜⋡ࡣ኱ࡁࡃኚືࡋ࡞ࡃ࡞ࡗࡓࠋ

⢭ᗘ࡟኱ࡁ࡞㐪࠸ࡣぢࡽࢀ࡞࠿ࡗࡓࠋ๓㏙ࡢ Feng ࡽ[13]ࡣࠊࣛࣥࢲ࣒ࣇ࢛ࣞࢫࢺࡢỴᐃᮌᩘᶵᲔᏛ⩦ᚋࠊ㜈್ࢆ 0.5 ࡜ࡋ᳨࡚ド⏝⏬ീ࡟ᑐࡋ࡚ࣄ

ࢆኚ໬ࡉࡏ㒔ᕷᬒほࡢศ㢮⢭ᗘࢆẚ㍑ࡋࡓࠋỴᐃᮌᩘࡀࡋࡓ⤖ᯝࢆ Fig.9 ࡟♧ࡋቑຍࡍࡿ࡜ศ㢮⢭ᗘࡶ㧗ࡲࡿࡀࠊ࠶ࡿᩘ௨ୖ࡟࡞ࡿ࡜ศࣀ࡛࢟࠶ࡿ࢜ࣈࢪ࢙ࢡࢺࢆண㢮⢭ᗘࡣᏳᐃࡍࡿࠋࡇࡢᏳᐃࡍࡿỴᐃᮌࡢᩘࡣࠊ࢜ࣈࢪ

ࡓࠋࡇࡢᅗ࠿ࡽẚ㍑ⓗⰋዲ࡟ࣄࣀ࢟ᯘࢆண4ࡋ࡚࠸ࡿࡇ静岡県東部における機械学習を用いたヒノキ林の推定

23―空中写真を用いた予備的調査―࡜ࡀࢃ࠿ࡿࠋࡋ࠿ࡋ࡞ࡀࡽࠊࣄࣀ࢟ᯘ࡛࡞࠸⟠ᡤ࡟ᑐࡍᖺᗘ⮬↛⎔ቃಖ඲ᇶ♏ㄪᰝ㸦➨㸵ᅇ㸧⥲ྜ࡜ࡾࡲ࡜

ࡿࣄࣀ࢟ணࡵᴗົሗ࿌᭩, 126pp, ⎔ቃ┬⮬↛⎔ቃᒁ⏕≀ከᵝཬࡧࣄࣀ࢟ᯘ࡛࠶ࡿ⟠ᡤ࡟ᑐࡍࡿ㠀ࣄࣀ࢟ࡀ࠶ࡿ⛬ᗘぢࡽࢀࡿࠋࡇࢀࡣࠊTable 1 ࡢഇ㝧ᛶ⋡ண࠿ࡽࡶ⿬௜ࡅࡽࢀࡿࠋࡇࡢࡼ࠺࡞ண

ᛶࢭࣥࢱ࣮, (2013).
[2]㛫㐪࠸ࢆపࡃࡍࡿ⎔ቃ┬⮬↛⎔ቃᒁ⏕≀ከᵝᛶࢭࣥࢱ࣮: ᳜⏕ㄪᰝࡇ࡜ࡀ௒ᚋࡢㄢ㢟࡛࠶ࡿࠋ௒ᅇࡢ⤖ᯝ࠿ࡽࡣࠊRGB ࡜ࡢᴫせ,<http://gis.biodic.go.jp/webgis/sc-007.html

HSV ࡟ࡼࡿ኱ࡁ࡞㐪࠸ࡣぢࡽࢀ࡞࠿ࡗࡓࠋ௒ᚋࡣࠊྠሙ>, (࢔ࢡࢭࢫ᪥: 2017/10/17).
[3]ᡤࡢ᧜ᙳ᫬ᮇ࡟ࡼࡿ㐪࠸ࢆ᳨ウࡍࡿࡇ࡜࡛ࠊࡼࡾỗ⏝ⓗ࡞ࣔࢹࣝ࡟௙ୖࡆࡿᚲせࡀ࠶ࡿࠋ㛗㇂ᕝዉ⨾, ᫍ㔝⩏ᘏ, ཎ៞ኴ㑻, 㙊ᙧဴ⛱, ୰ᑿ
຾ὒ: 㧗ศゎ⬟⾨ᫍࢹ࣮ࢱࢆ⏝࠸ࡓ᳃ᯘࢱ࢖ࣉุู : బ಴ᕷࡢ㔛ᒣᆅᇦࢆ౛࡜ࡋ࡚, ᳜⏕Ꮫ఍ㄅ,(a)30, (2013), 25-35.

(b)㻌

[4]

ᮧୖᣅᙪ: ➨ 8 ❶, ⏬ീฎ⌮(III)ศ㢮, ຍ⸨ṇே⦅,᳃ ᯘ ࣜ ࣔ ࣮ ࢺ ࢭ ࣥ ࢩ ࣥ ࢢ , J−FIC, (2004), pp.
107-117.

[5]㙊ᙧဴ⛱, ཎ៞ኴ㑻: ࢜ࣈࢪ࢙ࢡࢺ࣮࣋ࢫ⏬ീゎᯒ࡟ࡼࡿ᳜⏕ᅗ໬࡟࠾ࡅࡿ᳜⏕⏺ᢳฟ࡜ศ㢮⢭ᗘࡢ

Fig.9 ⏬ീศ㢮࡟ࡼࡿ⤖ᯝホ౯, ᳜⏕Ꮫ఍ㄅ, 27, (2010), 83-94.

(a) RGB ⏬ീ࡜ (b) HSV ⏬ീ࡟ᑐࡋ࡚ࠊ㜈್ 0.5 ࡟࠾ࡅࡿࣄࣀ࢟ᯘ㸦⅊Ⰽ㸧ཬࡧ㠀ࣄࣀ࢟ᯘ㸦ⓑ㸧ࡢண[6]㟼ᒸ┴⤒῭⏘ᴗ㒊᳃ᯘ࣭ᯘᴗᒁ᳃ᯘィ⏬ㄢ: 㟼ᒸ┴᳃ᯘ࣭ᯘᴗ⤫ィせぴᖹᡂ 29 ᖺᗘ∧, 170pp, 㟼ᒸ┴⤖ᯝ⤒῭⏘ᴗ㒊᳃ᯘ࣭ᯘᴗᒁ᳃ᯘィ⏬ㄢ, (2018).

ࢆ♧ࡍ㸦㯮⥺ࡣࣄࣀ࢟ᯘ⦕㸧[7]ᅜᅵᆅ⌮㝔: ᆅᅗ࣭✵୰෗┿㜀ぴࢧ࣮ࣅࢫ, <http://maps.gsi.go.jp>, (࢔ࢡࢭࢫ᪥: 2018/02/22).

せ⣙
[8]ᮏ◊✲ࡣࠊ㟼ᒸ┴࡛᭱ࡶᗈ኱࡞㠃✚ࡢࣄࣀ࢟ᯘ࡟ᑐࡍ㟼ᒸ᳃ᯘ⟶⌮⨫: ᐩኈ᳃ᯘィ⏬༊➨ 4 ḟᅜ᭷ᯘ㔝᪋ᴗᐇ᪋ィ⏬ᅗ, 㟼ᒸ᳃ᯘ⟶⌮⨫, (2010).

ࡿᶵᲔᏛ⩦࡟ࡼࡿศ㢮᪉ἲࢆ✵୰෗┿ࢆ⏝࠸᳨࡚ウࡋࡓࠋ[9]἟ὠᕷࠊఀ㇋ᕷࠊἙὠ⏫ࡢ㡿ᇦ࡟࠾࠸࡚ࠊࣄࣀ࢟ศ㢮ࡢᩍᖌࢹ࣮ࢱࢆసᡂࡋࡓࠋࡇࢀ࡟ࡣ RGB ཬࡧ HSV Ⰽ✵㛫㟼ᒸ┴᳃ᯘィ⏬ㄢ: ᳃ᯘ⡙࣭᳃ᯘィ⏬ᅗ, 㟼ᒸ┴᳃ᯘィ⏬ㄢ,(2015).

[10] Ἑ㑚┾, ㎷㔝࿴ᙪ, ㎷Ꮚ⿱஧: ᳃ᯘࡢᶞ✀࡟㛵ࡍࡿࢆ⏝࠸ࠊࡑࢀࡒࢀࡢᖹᆒ୪ࡧ࡟ᶆ‽೫ᕪࢆ≉ᚩ㔞࡜ࡋࡓࠋࣛࣥࢲ࣒ࣇ࢛ࣞࢫࢺ࡟ࡼࡿ⏬ീศ㢮ࡢ⢭ᗘࡣࠊỴᐃᮌᩘ㧗ศゎ⬟⾨ᫍ⏬ീ≉ᛶࡢศᯒ, ෗┿ཬࡧᩍᖌࢹ࣮ࢱᩘࡀቑຍࡍࡿ࡜࡜ࡶ࡟ศ㢮⢭ᗘࡣྥୖࡋࠊ

ࢺࢭࣥࢩࣥࢢ, 44, (2005), 82-90.

Ỵᐃᮌ 1,000 ᮏ௨ୖࠊᩍᖌࢹ࣮ࢱ 15,000 ಶ௨ୖࢆ㉺࠼㔞࡜࣮ࣜࣔ[11] Breiman L: Random forests, Machine Learning,ࡿ࡜࡯ࡰኚ໬ࡋ࡞࠸ࡇ࡜ࡀ☜ㄆࡉࢀࡓࠋỴᐃᮌ 2,000 ᮏࠊ45, (2001), 5-32.

ᩍᖌࢹ࣮ࢱ 24,000 ಶ࡛ศ㢮ࢆ⾜ࡗࡓ⤖ᯝࠊRGB Ⰽ✵㛫[12] Ἴ㒊ᩧ: ࣛࣥࢲ࣒ࣇ࢛ࣞࢫࢺ, ◊✲ሗ࿌ࢥࣥࣆ࣮ࣗ࡟ࡼࡿศ㢮࡛ࡣ AUC ࡢ್ࡣ 0.83ࠊHSV Ⰽ✵㛫࡟ࡼࡿศࢱࣅࢪࣙࣥ࡜࢖࣓࣮ࢪ࣓ࢹ࢕࢔㸦CVIM㸧, 2012,㢮࡛ࡣ 0.84 ࡜Ⰻዲ࡞⤖ᯝࢆᚓࡿࡇ࡜ࡀ࡛ࡁࡓࠋRGB Ⰽ(2012), 1-8.

✵㛫࡜ HSV Ⰽ✵㛫࡟ࡼࡿศ㢮⢭ᗘ࡟኱ࡁ࡞㐪࠸ࡣぢࡽ

[13] Feng Q, Liu J, Gong J: UAV remote sensing forࢀ࡞࠿ࡗࡓࠋ௒ᚋࠊᗈᇦ࡟ㄪᰝࡋ㟼ᒸ┴඲ᇦ࡟࠾ࡅࡿᶵ

urban vegetation mapping using random forestᲔᏛ⩦࡟ࡼࡿࣄࣀ࢟ᯘࡢ᥎ᐃࢆ┠ᣦࡍணᐃ࡛࠶ࡿࠋ

and texture analysis, Remote Sensing, 7, (2015),
1074-1094.
[14] Ma L, Fu T, Blaschke T, Li M, Tiede D, Zhou Z,ㅰ㎡ᮏ◊✲ࢆ㐍ࡵࡿ࡟࠶ࡓࡾࠊ㟼ᒸ᳃ᯘ⟶⌮⨫ࡼࡾᅜ᭷ᯘMa X, Chen D: Evaluation of feature selection㔝᪋ᴗᐇ᪋ィ⏬ᅗࠊ㟼ᒸ┴᳃ᯘィ⏬ㄢࡼࡾ᳃ᯘィ⏬ᅗ࡜methods for object-based land cover mapping of᳃ᯘ⡙ࡢࢹ࣮ࢱࢆࡈᥦ౪㡬ࡁࡲࡋࡓࠋࡇࡇ࡟グࡋ࡚ㅰពunmanned aerial vehicle imagery using randomࢆ⾲ࡋࡲࡍࠋ

forest and support vector machine classifiers,
ISPRS International Journal of Geo-Information,
6, (2017), 51: doi: 103390/ijgi6020051.

ཧ⪃ᩥ⊩[1]⎔ቃ┬⮬↛⎔ቃᒁ⏕≀ከᵝᛶࢭࣥࢱ࣮: ᖹᡂ㸰㸲5