中文摘要：

为利用遥感影像数据在区域尺度上实现快速、准确地监测小麦白粉病的发生、发展情况，该研究基于环境与灾害监测预报小卫星（HJ-1A／1B）数据对地表温度（landsurfacetemperature，LST）进行反演、提取4个波段反射率数据并构建7个植被指数。耦合K-mean和Relief算法对小麦白粉病遥感特征进行筛选。通过支持向量机（supportvectormachine，SVM）与小波特征（Gabor）结合SVM（GaborSVM）的方法分别建立河北省晋州市小麦白粉病发生监测模型，并对2种模型的监测精度进行对比。结果表明：归一化植被指数（normalizeddifferencevegetationindex，NDVI）、比值植被指数（simpleratioindex，SR）和地表温度3种特征参量可较好地表征小麦白粉病的发生情况，GaborSVM的总体精度达到86．7％，优于SVM的80％。因此，小波分析与支持向量机结合的方法可用于基于卫星遥感影像的大面积病害监测，对提高病害监测精度具有重要应用价值。

英文摘要：

Wheat powdery mildew is one of the main serious diseases for winter wheat. A fast and accurate monitoring of the disease at a regional scale plays a vital role in reducing yield loss. Remote sensing data has great advantages over traditional data in disease monitoring, including simpler operation, more real-time and higher resolution. In this study, Chinese HJ-1A/1B data with high revisit frequency and 30 m spatial resolution was used to inverse Land Surface Temperature （LST）, extract four-band reflectance data, and build seven vegetation indices. These indices should be filtrated to improve accuracy of the model due to redundancy of them. Then, we implemented screening features with the combination of Relief and K-mean algorithm. Relief algorithm which can provide the basis for feature evaluation, so features were ranked in descending order judged by feature weights in preparation for the next process. Clustering accuracy obtained by K-mean algorithm. According to the weight of the feature, the features clustered in turn to perform K-mean analysis. Then the cluster with the highest precision was picked out, and we finally got the normalized difference vegetation index （NDVI）, Simple vegetation index （SR） and surface temperature （LST） as the feature set. Wavelet feature can decompose the data in multi-scale and multi-direction, which can highlight the sensitive factor of vegetation index to a certain extent. Forty wavelet functions were constructed from five scales and eight directions, and made them convolve with features. Because there were too many wavelet features after convolved, the independent T-test samples were used to obtain the most sensitive wavelet feature of disease and the corresponding wavelet kernel function. After this process, three features corresponding to vegetation indices were available. These three wavelet features were used as input variables of the model. Support vector machine is a kind of machine learning method based on statistical learning theory. Its core idea is to