基于特征波段的SPOT-5卫星影像水稻面积信息自动提取的方法研究

为了快速、准确地从遥感影像上提取水稻信息，满足国家农情遥感监测系统要求，以黑龙江省852农场水稻提取为例，利用SPOT-5卫星影像数据，分析了水稻和其它背景地物的光谱特征，发现利用原有波段难以提取复杂的水稻信息，因此利用植被特征波段：归一化植被指数（NDVI）作为新波段融入原始影像中，在增加有效信息量的同时运用简单决策树模型提取水稻信息，并参照地块现状矢量图进行精度评价。结果表明，该方法的总体提取效果较好，其提取精度与通常的监督分类方法相比有了较大的提高，只是在水稻和玉米交界处有误判现象。

Decision Tree Algorithm of Automatically Extracting Paddy Rice Information 5from SPOT-5 Images Based on Characteristic Bands

For meeting the demand for large-scale agricultural monitoring system with remote sensing technology,extracting paddy rice information on the remote sensing image must be rapidly,precisely and reliable conducted.In this paper,paddy rice identification with SPOT-5 image was taken as an example on the 852 farm in Heilongjiang province of China.Firstly,the spectral characteristics of paddy rice and other six land-use types in this area were analyzed to find the possibility of extracting of paddy rice from the background.The results show it is difficult to distinguish paddy rice information from background on the SPOT-5 images because of complexity of spectrum and lack of band information.Secondly,taking those into account,characteristic bands for paddy rice extraction were proposed and merged into SPOT-5 images in order to increase spectral information and improve the separability.Thirdly,a simple model of decision tree was applied to extract paddy rice information.Finally,the results were checked by visual and statistica1 accuracy assessment.The results suggest that the model based on characteristic bands is simple and effective,and the accuracy by the model is much higher than that by the supervised classification method.However，some pixels in the neighborhood area between paddy rice and corn were misjudged.