基于SPOT-5卫星影像的灌区作物识别

高分辨率卫星影像是作物精确分类和评估的重要数据源，在农作物种植规划、估产等领域具有重要的应用价值。本研究利用分辨率为2.5 m的SPOT 5影像分析张掖市盈科灌区的作物分布状况，同时分别生成分辨率为10和30 m的影像，用于尺度验证。最终得到研究区作物分类图，所用方法主要有最小距离法、马氏距离、最大似然法、光谱角制图仪（SAM）和支持向量机（SVM）。Kappa系数分析表明，最大似然法和SVM的分类效果好于其它分类器，分别为0.871 9和0.862 5，但这两种方法的统计量无明显区别；分类图精度评价表明，基于最大似然法的分类图总体精度最高，为90.6%；随像元空间尺度的增加，分类精度未产生明显变化。研究结果表明，最大似然法和SVM技术可以与SPOT 5影像结合，用于作物类型识别和作物面积估算。

Crop identification in the irrigation district based on SPOT-5 satellite imagery

High resolution satellite imagery is one of important data sources for crop monitoring and assessment, and has important application values in the field of crop planning and yield estimation. Distribution of crops in the Yingke irrigation district of Zhangye City was analyzed by using a combined data of SPOT 5 image, obtained in 2008, with four spectral bands (green, red, near infrared and short wave infrared) and 2.5 m pixel size covering. Two images with pixel sizes of 10 m and 30 m were also generated from the original combined image to simulate coarser resolution satellite imagery. Five supervised classification techniques, including minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper (SAM) and support vector machine (SVM), were applied to identify crop types in the study. The effects of pixel size on classification results were also examined. Kappa analysis showed that the maximum likelihood and SVM, though there were no statistical differences between them, performed better than those from other classified methods and the value of Kappa were 0.871 9 and 0.862 5, respectively. Accuracy assessment showed that the maximum likelihood gave the best result with overall accuracy values of 90.6%. The results also showed that increasing pixel size from 2.5 m to 10 m or 30 m did not significantly affect the classification accuracy for crop identification. Overall results indicate that SPOT 5 image in conjunction with maximum likelihood and SVM classification techniques can be used for identifying crop types and estimating crop areas.