Comparison of treebased classification algorithms in mapping burned forest areas

Matci, Dilek Kucuk (author), Comert, Resul (author), Avdan, Ugur (author)

In this study, we compared the performance of tree-based classification algorithms Random Forest (RF), Rotation Forest (RotF), J48, The Alternating Decision Tree (ADTree), Forest by Penalising Attributes (Forest PA), Logical Analysis of Data Algorithm (LADTree) and Functional Trees (FT) for mapping burned forest areas within the Mediterranean region in Turkey. Object-based image analysis (OBIA) was performed to pan-sharpened the Landsat 8 images. Four different burned areas, namely Kumluca, Adrasan, Anamur, and Alanya, were used as study areas. Kumluca, Anamur, and Alanya regions were used as training areas, and Adrasan region was used as the test area. Obtained results were evaluated with confusion matrix and statistically significant analysis. According to the results, FT and RotF produced more accurate results than other algorithms. Also, the results obtained with these algorithms are statistically significant.

• Language: English
• Keywords: tree-based algorithm, machine learning, remote sensing, dupervised vlassification, Landsat
• Typology: 1.01 - Original Scientific Article
• Organization: FGG - Faculty of Civil and Geodetic Engineering
• Year: 2020
• Number of pages: Str. 348-360
• Numbering: Letn. 64, št. 3
• PID: 20.500.12556/RUL-132803
• UDC: 004.421:528.8:630
• ISSN on article: 0351-0271
• DOI: 10.15292/geodetski-vestnik.2020.03.348-360
• COBISS.SI-ID: 33160707
• Publication date in RUL: 03.11.2021

Record is a part of a journal

• Title: Geodetski vestnik. glasilo Zveze geodetov Slovenije
• Shortened title: Geod. vestn.
• Publisher: Zveza geodetov Slovenije
• ISSN: 0351-0271
• COBISS.SI-ID: 5091842

Secondary language

• Language: Slovenian
• Title: Primerjava drevesnih algoritmov razvrščanja pri zajemu območij pogorelih gozdov

Abstract

V študiji primerjamo rezultate različnih drevesnih algoritmov razvrščanja območij za zajem pogorelih gozdov v sredozemskem delu Turčije, to so naključni gozd (angl. random forest), rotacijski gozd (angl. rotation forest), J48, izmenično odločitveno drevo (angl. alternating decision tree), gozd z izločanjem atributov (angl. forest by penalising attributes), logična analiza podatkovnih algoritmov (angl. logical analysis of data algorithm) in funkcionalni gozd (angl. functional forest). Izvedli smo objektno analizo (OBIA, angl. object-based image analysis) izostrenih satelitskih podob Landsat 8. V študijo so bila vključena štiri pogorela območja oziroma regije, to so Kumluca, Adrasan, Anamur in Alanya. Kumluca, Anamur in Alanya so bili izbrani za učenje, medtem ko je bil Adrasan uporabljen kot študijsko območje. Rezultate smo ovrednotili z matriko razvrstitev in statističnimi analizami. Rezultati so bili najboljši pri uporabi algoritmov funkcionalnih dreves in rotacijskih dreves, pri čemer so se rezultati izkazali tudi statistično značilni, medtem, ko so bili pri drugih algoritmih slabši.

• Keywords: drevesni algoritmi, strojno učenje, daljinsko zaznavanje, nadzorovana klasifikacija, Landsat