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Image classification is an important technology in the application of remote sensing. Traditional methods of image classification are based on low or medium-resolution images, and the accuracy of classification is always very low. In recent years, high-resolution remote sensing images have significant improvements, but there is still no good method of classification. Studies showed that the accuracy of classified high-resolution images is even lower than that of low or medium -resolution images by traditional classification methods. This turns out that traditional classification technologies appeared to have serious error when using high-resolution images. In this paper, a method of multi-feature classification was introduced to high-resolution remote sensing image, thus avoiding the method of single-feature and pixel-based classification. In this method, pixel-based high-resolution images are changed into object-based images by segmentation. Models of area, perimeter, length, width, symmetry, ratio of length and width, rectangular fit and compactness were established to measure features of segmented objects. More over, the new method of using spectral and texture features to classify high-resolution images was completed. The result showed that the accuracy of image classification can be up to 91.6% by the multi-featured classification, which proved to have improved high-resolution remote sensing image classification.
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