作物的种植面积能够反映农业生产在空间范围利用农业生产资源的情况，是进行农业结构调整的依据，是研究粮食区域平衡，预测农业资源综合生产能力与人口承载能力的重要数据源。及时、准确的获取区域作物种植面积信息及其空间分布状况，对于准确估计和预测作物产量，加强作物生产管理，优化作物种植空间格局，确保我国粮食安全具有重要意义；利用遥感数据监测农作物的种植面积与传统的地面调查相比，可以较大程度的排除人为因素的干扰，客观性强，并且可以极大的节省人力、物力、财力和时间，具有很高的经济效益和社会效益。 以往的农作物种植面积遥感测量是基于像元进行的，将每个像元都作为纯净像元处理，很少考虑类别内部的结构纹理以及相邻像元之间的关联信息。当地块面积比较小时，基于像元分类后产生的误差严重影响了面积提取、统计的准确性。基于地块分类方法与传统的基于像元分类方法的本质区别是针对影像对象进行分类，而不是对像素进行分类，目的是提取真实世界的地物对象，形状和类别都要正确。在利用光谱信息的同时，考虑了影像对象的空间信息，融入了对象的纹理特征与邻域信息，整个过程更符合人类认知事物的过程，分类的精度得到了提高。 本研究以玉米种植面积遥感测量为目标，选取种植结构复杂的农业区河南省原阳县为方法实验区，通过高分辨率影像建立地块边界数据，以多时相TM影像为核心数据源，对TM数据进行预处理，结合NDVI（Normalized Difference Vegetation Index，归一化差值植被指数）及特征波段信息采用决策树方法对实验区进行预分类，初步获取玉米种植范围；将玉米预分类结果与耕地地块数据空间叠加分析，以地块内玉米的预分类面积比例为分层标志，建立分层模型，结合交通数据，布设野外样方；采用遥感影像与车载GPS结合的方式，设计合理的野外调查路线，开展野外样方实测工作，获取样本地块内的玉米种植比例；通过高分辨率混合地块边界，提取混合地块的多时相TM影像特征向量信息，将光谱反射率、植被指数、纹理进行特征组合，随机选取不同比例的训练样本地块，构建SVM混合地块分解模型，并以野外目视解译获得的混合地块玉米种植面积百分比为标准值，对玉米种植面积测量结果进行精度评价，并与基于光谱单源信息的SVM混合地块分解法和多源信息复合的决策树混合地块分解法进行对比分析，探索适用于不同种植结构的基于SVM混合地块分解的玉米种植面积遥感测量方法。研究结果表明： （1）通过人工数字化的方式在高分辨率融合影像上建立耕地地块数据库，能够保证地块边界与实际地块边界相对应，便于野外测量工作的开展，但是对于大范围玉米种植面积测量，这种方法的工作效率远远低于图像自动分割技术，因此在今后的工作中，有必要探索能与实际地块边界完全匹配的图像自动分割技术。 （2）基于多源信息复合的SVM混合地块分解法的精度优于基于光谱单源信息的SVM混合地块分解法，说明加入植被指数信息、纹理信息，对分类精度有一定的改善。 （3）基于多源信息复合的SVM混合地块分解法的精度优于基于多源信息复合的决策树混合地块分解法，说明SVM更具备高维数据向量的推广能力，也更为适合训练样本较少的情况。 （4）所选实验区为河南省原阳县，虽然精度得到了一定的改善，但是如何将该种方法推广到整个省的面积测量成为今后研究的重点。

The corn planting area can reflect how the agricultural production make use of the agricultural resource in the space, by which we can adjust the agricultural structure, and can forecast the comprehensive productivity of agricultural resource and the carrying capacity of population. Accurate and timely updated-information for corn planting area is essential to yield estimation, agricultural management and food security. Comparing to the traditional ground investigation, using remote sensing to measure the planting area of corn can exclude the human disturbance greatly, and can save manpower, material resources, financial and time, which will have great economic and social benefits. Most current automatic classification techniques to obtain crop planting area from digital imagery operate on a per-pixel basis in isolation from other pertinent information. Therefore, per-pixel techniques often yield results with limited reliability on areas where parcel size is too small. The reliability of image classification can be improved by including a priori knowledge about the contextual relationships of the pixels in the classification process. For per-field classification, the geometry of the boundaries defines the spatial context between the pixels contained within, and enables those pixels to be processed in coherence. A final decision on the class assignment of pixels within each field is taken based on the coherent processing of these pixels. This is unlike per-pixel classification where the decision for each pixel is reached independently, and it can provide the best results. In order to obtain the planting area of corn, this study chose yuanyang county as experimental area and established field background database by high-resolution image. Then we pretreated the data and used the NDVI and reflectivity to carry through the classification according to the multi-temporal TM images. Then we integrated the classification results and vector field boundary, taken the area proportion of corn in the field as the hierarchical model to establish the hierarchical model, then we went out to investigate the real area proportion of corn in the selected field. In virtue of the vector field boundary, we extracted the eigenvector on TM images, then we integrated the spectral reflectivity, vegetation-index and texture to compose SVM mixed-field decomposed model. At last, we used the investigate results as standard to judge the per-field classification results, and compared the per-field classification results to the single-source data and per-pixel classification results. By analyzing the accuracy, this paper drew main conclusions as follows： (1) We established field background database on high-resolution image, which could insure the field boundary corresponding to the real field, and was easy to open surveying field-work. But for measuring corn area greatly, the work efficiency of this method was far worse than division-technique automatically, so how to improve the accuracy of the division-technique is the stress for our future work. (2) We got higher accuracy of corn planting area measuring when using multi-source data than single-source data, which showed that vegetation-index and texture could improve the accuracy of classification. (3) We got higher accuracy of corn planting area measuring when using SVM than the decision-tree classification, which showed that SVM had the ability to extend the high-dimension data, and fit classification with a few-swatch. (4) We selected yuanyang country as our experimental area and had a good accuracy ,but how to extend this method to the whole province is the stress for the future.