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Satellite sensing image analysis by integrating neural network with knowledge reasoning technique

In this paper, a new classified method for satellite sensing data is proposed by using both a neural network and knowledge reasoning technique. Neural network model can better distinguish land of level I, but if more subdivision needed, it seldom get satisfied result. Knowledge reasoning system can use human geographical knowledge to improve the classification results, but it needs a large amount of assistant knowledge to classify the data correctly. The new method makes use of the advantages of both the neural network and knowledge reasoning technique, and fulfils layered intelligent extraction of linear object and plane-like object for satellite sensing image. It firstly extracts water and road information by neural network and pixel-based knowledge post-processing method, then remove them from original image, and then segments other plane-like object by neural network model, and respectively computes their features, including texture, elevation, slope, shape etc., then extracts them by polygon-based uncertain reasoning method. At last experimental results indicates that the new method outperforms the single neural network method and moreover avoids the complexity of single knowledge reasoning technique.