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摘要： 准确提取水稻种植面积是探讨气候变化背景下水稻生产与粮食安全的重要前提。我国南方的水稻种植区域,地块破碎且受云雨天气影响严重,如何充分利用有限时相的数据获得较高精度的水稻面积提取是亟需解决的关键问题。提出了一种利用两个时相的数据,通过构建差值特征突出水稻物候变化的特点,并与随机森林算法结合高精度提取水稻种植面积的方法。将之应用于湖南省常德市鼎城区的水稻种植面积提取,结果表明:采用本方法进行水稻提取的最终总体精度达到93.01%,Kappa系数0.91,与单时相提取结果相比,总体精度提高了近3%。为了进一步分析差值特征对其他分类器的改进效果,分别将差值特征与决策树和随机森林组合,并分析了两种组合提取水稻的精度。研究发现构建的差值特征能够有效反映植物的生长状况,增加地物的可区分性,可为对象的分割及分类提供更多有用的信息,能够有效改善水稻种植面积的提取精度。 关键词： 水稻提取;  特征波段;  多时相;  差值波段     Abstract: To accurately extract the growing area of paddy rice is a significant premise of paddy rice production and food security under the background of climate change.based on the current situation that paddy rice extraction is beset with difficulties in southern china,where clouds and rain appear in high frequency during growing seaon,how to take full advantage of the limited images to obtain accurate paddy rice planting area is a desiderated problem.In this study,we combined remotely sensed data from two different dates and brought out D\|value bands,using object\|oriented Random Forest to achieve the goal of rice extraction.The D\|value bands,indicating the difference between a character derived from two different time phases,can be generated from traditional characteristic bands including vegetation indexes,water index,prominent component analysis and Tasseled Cap results,as well as the original bands.We applied this method to extract paddy rice planting area in Dingcheng District,Changde,Hunnan Province,China,and results show that,the accuracy of paddy rice extraction was improved to 93% by 3 percent compared with single\|phased method,and the kappa coefficient reaches 91 in the study area.To further analyze the effect of D\|value bands in other classifiers,we compared the accuracy of combination of D\|value bands with decision tree and Random Forest,separately.Results show that the D\|value bands provides infromation in both subject segementation and classification,which can effectively improve the accuracy of paddy rice planting area extraction.  

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