Classification of Vegetation and Soil Using Imaging Spectrometer Data

Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. New algorithms have been developed for hyperspectral data classification lately, but also traditional classification algorithms have often been used. This study compares different classification algorithms for classification of vegetation using imaging spectrometer data. The test area located in southern Finland was imaged by an AISA airborne imaging spectrometer using 17 visual and near infrared bands. The area included lakes, rural areas, cultivated fields and forests. The area was classified into seven different vegetation and soil types. The effects of various classification algorithms and different training areas were investigated. Besides, the reflectance spectra of different plants were examined and compared under varying illumination. Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM) and Spectral Unmixing algorithms developed for hyperspectral data were used in the classification. Besides, the data was classified using conventional algorithms as Minimum Distance and Maximum Likelihood classifiers that have often been used for multispectral data classification in the past. The dimension of the data was decreased by principal component analysis before using conventional classifiers. Reference spectra for SAM, SCM and Spectral Unmixing were collected from the training sites of the data. Two methods were used in gathering the reference spectra. The reference spectra were chosen from the reflectance of individual image pixels or they were calculated from pixels of the training sites. If individual pixel was chosen accurately, it led to better classification results. Maximum Likelihood classifier led to good results as well, but it requires more computation time. The overall accuracy of the Maximum Likelihood classification was 91 percent, but the results deteriorated under varying illumination. SAM and SCM were faster and they led to better classification results in poor illumination. The hardest part in Spectral Unmixing classification was finding suitable reference spectra from mixed pixels. When the essential spectra were found, the classification led to good results, although the results varied between different classes.