Selection of Landsat 8 OLI Band Combinations for Land Use and Land Cover Classification
| Autor: | Zhiqi Yu, Chen Zhang, Liping Di, Md. Shahinoor Rahman, Eugene Genong Yu, Junmei Tang, Li Lin, Haoteng Zhao, Ruixing Yang, Ziheng Sun, Juozas Gaigalas |
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| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
Land use Computer science 0211 other engineering and technologies Feature selection 02 engineering and technology Land cover 01 natural sciences Support vector machine Statistical classification Multicollinearity Satellite Independence (probability theory) 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Agro-Geoinformatics |
| DOI: | 10.1109/agro-geoinformatics.2019.8820595 |
| Popis: | Land use and land cover (LULC) classification using satellite images is an important approach to monitor changes on earth. To produce LULC maps, supervised classification methods are often used. For many supervised classification algorithms, independence of features is an implied assumption. However, this assumption is rarely tested. For LULC classification, using all bands as input features to models is the default approach. However, some of the bands may be highly correlated, which may cause model performances unstable. In this research, correlations and multicollinearity among multi-spectral bands are analyzed for four major LULC types, i.e. cropland, forest, developed area and water bodies. Guided by the correlation analysis, different band combinations were used to train Support Vector Machines (SVM) for four-class LULC classification and the results were compared. From our experiments, band 4, 5, 6 is the best three-band combination and band 1, 2, 5, 7 is the best four-band combination which achieved almost identical performance as using all bands for LULC classification. |
| Databáze: | OpenAIRE |
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