Geographic Object-Based Image Analysis and linear unmixing are common methods in image classification. The purpose of this study is to analyze the classification efficiency by integrating these two methods in the mountain area. This research selected Jiangle County, Fujian, as a study area. Two Landsat8 OLI images, which covered the county, were used. Linear spectral mixture model, multi-scale segmentation, and decision tree were applied in the classification. After image preprocessing, linear spectral mixture model was used to unmix the image into three fraction images-vegetation, shade, and soil. The principal component analysis and tasseled cap transformation were used to derived three principal components and the brightness, wetness, and greenness. Multi-scale segmentation is applied by eCognition. Under scale 40, the image was divided into vegetation and non-vegetation area, then under scale 20, the vegetation area was divided into different types by integrating the fraction with different methods. The accuracy assessment of the classification map was done using the forestry resource survey and the high-resolution image of Google Earth. This study indicated that the unmixed bands could improve the classification accuracy. The overall classification accuracy was 92.40% with a Kappa coefficient of 0.9032. Therefore, there is a conclusion that this approach is an efficient way to classify different plantation.
Keywords: Decision tree; GEOBIA; Multi-scale segmentation; Pixel unmixing; Remote sensing.