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Urban vegetation, which scatters in gardens, settlements and streets etc., appears small, often fragmented, linear patches. Automatic vegetation detection in color remote sensing imagery is useful for obtaining more timely and accurate information. In this paper, a new biological vision methodology based on visual attention theory and adaptive resonance theory (ART) is presented to automatic detect urban vegetation in color remote sensing imagery. The central rationale of the method is that vegetation information is from the double-opponent saliency map and then gets object-based classification. Without a priori knowledge of image content, the image can be segmented into vegetation and other object through the unsupervised learning and self-organization fuzzy ART neural network. Experimental results indicate that our method performs much better than eCognition○R .

Biological Vision for Urban Vegetation Detection in Color Remote Sensing Imagery