The pixel- and object-level classification methods are investigated separately, while the hybrid of them has not been explored. This paper tries exploration on the hybrid of pixel- and object-level classification, and proposes synthesis classification method for remote sensing image based on improved pixel- and object-level classification. Firstly, an improved RBF neural network classifier is proposed to obtain the pixel-level classification result, and a hierarchy classification model based on improved fuzzy support vector machines and decision tree is utilized to obtain the object-level classification result. Then a specific synthesis algorithm ofpixel- and object-level classification is proposed to obtain the synthesis classification result. The experiments show the synthesis classifi- cation method can improve the accuracy of classification result effectively and provide more accurate classification result than single pixel- or object-level method.%像素级和对象级的分类研究分别作为两个独立的方向开展，二者的结合与优势互补还没有引起关注。对像素级和对象级分类方法的结合进行探索，提出基于改进的像素级和对象级的遥感影像合成分类方法。首先，以一种改进的RBF神经网络分类器进行像素级分类、以一种基于改进模糊支持向量机和决策树的层次分类模型进行对象级分类，获得多层次分类结果。然后，提出具体的像素级分类与对象级分类的合成算法，对多层次分类结果进行合成。试验表明，合成分类方法能有效地提高分类结果的精度，提供比单一像素级方法或对象级方法更准确的分类结果。
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