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Automatic Forest Extraction Method Based on Self-Organizing Map Algorithm Using Gf-2 Images

With more and more high-spatial-resolution images being applied to interpret objects and to extract information, machine learning methods have emerged as solutions for the large amount image data's processing and analyzing. But how to improve the efficiency and accuracy of interpretation and extraction becomes a problem. For China Geological Survey's national forest survey project, we proposed an automatic extraction method to extract forest area from high resolution images of GF-2 satellite based on self-organization map (SOM) which is an artificial neural network (ANN) method using unsupervised learning to produce a low-dimensional, discretized representation of the input space of the training samples. Also, the proposed method was applied to three different areas to verify its accuracy. Compared with those of human-computer interactive interpretation, the results of the proposed method had high classification accuracy for extracting forest information.