As widely used today, high resolution image becomes a useful data source for forest inventory because it can show detailed information of land-cover types which is so helpful in interpreting process. And in the application of high-resolution images, the toughest problem is to find the effective characteristics group to separate each class accurately. In this paper, we tried an object-based method to get the whole forest distribution of the study area. Combining segmentation and decision tree feature selection tool, we tried to find a convenient and effective way to select useful information from such many characteristics brought by ―super-pixels‖ after segmentation. Compared with the traditional pixel-based classification method, we found that object-based method was more appropriate not only for its nearly 10% higher classification accuracy but also providing with more detailed information lying in the image data that help.
Polar metric synthetic aperture radar (PolSAR) image classification is an important technique in the remote sensing area, has been deeply studied for a couple of decades. This paper proposes a new approach for segmentation and classification of PolSAR datain two steps. First, segmentation is performed based on spectral graph partitioning using edge information. Graph partitioning process is completed using the normalized cut criterion. Then, classification is performed based on the object level. We use Cloude and Pottier‟s method to initially classify the PolSAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The advantages of this method are the automated classification, and the interpretation of each class based on the region‟s scattering mechanism. We tested this object-based analysis on our study area. It showed that this result well overcome the pepper-sault phenomenon appearing in the one using traditional pixel-based method, providing robust performance and the results more understandable and easier for further analyses
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