中文摘要：

针对对像元尺度上独立于分类方法的不确定性评价的需要和对数混合熵函数在评价遥感影像分类不确定性中存在的不足，该文提出了一种基于参数型指数混合熵模型的农业遥感影像分类不确定性评价方法。研究首先对指数混合熵函数进行改进，推导出参数型指数混合熵函数并确定出适合于评价农作区遥感影像分类的参数；然后，使用该函数建立一种像元尺度上独立于分类的不确定性评价模型；最后，将该模型应用于空间分辨率退化10倍的SPOT-5影像中，并使用原始影像对评价结果进行验证。试验结果表明，当模型中参数型指数混合熵函数的参数分别为4和1时，该函数比对数混合熵函数更好地统一了模糊性和随机性，熵值范围提高了2.11倍。该模型不确定性评价结果与原始影像3种分类的不确定像元比例相关系数分别为0.60、0.66、0.70，评价结果较为准确。因此，该模型可以在像元尺度上独立于分类方法将地物类别相对复杂的农业遥感影像分类不确定性更为精确地表达出来，为确保农作物种植面积提取、区域产量遥感估测精度提供了有力支撑。

英文摘要：

Uncertainty is the most important factor which affects the quality of remote sensing image classification （RSIC）, research on uncertainty in RSIC is a cutting-edge, hot topic in remote sensing application study. Study of RSIC gradually developed from simple qualitative and non-positioning research into specific quantitative and positioning research. At present, a RSIC uncertainty evaluation model based on pixel scale and independent of the classification method should be established. In recent years, some scholars began to use hybrid entropy model to evaluate uncertainty in RSIC. However, these studies did not focus on a particular area and find out a suitable entropy function. How to find out a suitable entropy function which better integrate both fuzziness and randomness and facilitate a wider range of entropy values has always been a difficult point of research. From the discussion above, this paper established a method for evaluating uncertainty in agricultural RSIC based on exponential hybrid entropy in parametric form （EHEP）. In this study, firstly, the exponential hybrid entropy function was deduced in parametric form, and EHEP was obtained. EHEP is improvement of hybrid entropy which has the shortcoming of lacking adjustable parameters. After adjusting parameters, entropy function can better integrate fuzziness and randomness and facilitate a wider range of entropy values, so this function is suitable for evaluating RSIC uncertainty. Moreover, by the research on the relationship between the parameters and the entropy function surface, the paper ascertained parameters which are suitable for evaluating uncertainty in farming area RSIC. Secondly, EHEP was used to establish a RSIC uncertainty evaluation model based on pixel scale and independent of the classification method, in order to offer elicitation to simulation of the uncertainty transferred in space model, and to help fill a vacancy in uncertainty evaluation model based on pixel scale and independent of the classification method. Lastly, the E