Detecting subpixel woody vegetation in digital imagery using two artificial intelligence approaches
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Small strips or patches of woody vegetation, typical landscape elements in many farming areas, are frequently not detected by standard computer-assisted classification of digital satellite imagery because such landscape elements are smaller than the pixel size and are mixed with other classes. This study essentially compares two artificial intelligence approaches-machine-vision and neural-network methods-developed to improve classification accuracy for this mixed pixel problem. Simulated multispectral and panchromatic SPOT HRV imagery of lowland Britain was used to test both methods. Compared to standard supervised multispectral classification, both methods yield significant improvements in detecting subpixel woody vegetation. in general, the machine-vision approach outperformed the neural-network approach. However, because each method generated different types of misclassifications, a classification map representing only the woody vegetation found by both methods provided the results with the least amount of overall error.