A study on hyperspectral image classification of boreal area

• Main Authors: Lin-Mu Guo, 郭麟霂
• Other Authors: Tian-Yuan Shih
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/95672287356029736329

碩士 === 國立交通大學 === 土木工程系 === 88 === A Study on Hyperspectral Image Classification of Boreal Area Student: Lin-Mu GuoAdvisor: Dr. Tian-Yuan Shih Institute of Civil Engineering National Chiao Tung University Abstract Boreal forests cover less then 4% of the total area of the earth, but the oxidation inside marshes produce lots of carbon dioxides, this might cause a global climate change, such as the greenhouse effect. As a result, the ground coverage map of boreal area becomes an important reference data for other researches. This study applies a frame of AVIRIS image of boreal area for land cover classification. To increase the separability, and reduce the data dimensionality, choosing a good classifier and removing the high correlation between bands are important factors that affect the result of hyperspectral image classification. In this study, LPF (Low Pass Filter), PCA (Principle Component Analysis) and MNF (Minimum Noise Fraction) are applied to obtain the best spectral combination for classification. The results from different classification approaches are then compared. According to the result of this research, Low Pass Filter processing can improve the classification accuracy effectively. But combining with MNF or PCA transformation can obtain higher accuracy in lower dimensionality. In general, MNF transformation is better then PCA transformation. Regarding to classification method, Maximum Likelihood Classifier performs much better then Minimum Distance Classifier or Spectral Angle Mapping classifier.