遥感图像尤其是高空间分辨率（1—4m）遥感图像在土地利用和土地覆盖变化方面有着广阔的应用前景，传统高空间分辨率遥感图像信息提取方法存在精度和分类效率低的不足。本文提出的基于SVM的分类方法，复合光谱、纹理和结构信息等多源数据信息，对IKONOS高空间分辨率图像进行分类，并与最大似然法和单源数据（光谱）SVM分类结果进行定性和定量比较分析。研究结果表明，多源数据复合的SVM高空间分辨率遥感图像分类方法，能够有效解决单源数据信息图像分类效果破碎的问题；总精度达到68．38％，Kappa达到0．5993；对高维输入向量具有高的推广能力；比单源信息的SVM和最大似然方法图像分类精度更高，适合高空间分辨率遥感图像分类。

The RS miage shows a very promising perspective forurban land-coverand land-use classification,\nparticularlywith very high resolution(1—4m) satellite mi ages, while the traditional extractionmethods of the\nhigh spatial resolution mi age has the shortcomings of the low accuracy and classification efficiency. This paper\ndealswith the high spatial resolution mi age(IKONOS) classification based on the SVM method integrating the\ninformation of spectra,l texture and structure. And comparing to the results based onMaxmi um Likelihood and\nSVMmethodwith single-source data, this shows thatthe high spatial resolutionRS mi age classification based on\nSVMMethod with multi-source data can solve the mi age classification fragmentation which is based on the\nsingle-source data, spectrum, and has the good generalization ability with the high dmi ension vector. It has\nmore accuration than themaxmi um likelihoodmethod and SVM based on the single source data, adapts to the\nhigh spatial resolution RS Image classification.