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摘要： 快速准确掌握新增建设用地信息对城镇化监测研究具有重要意义。基于后验概率变化矢量检测的土地覆盖更新方法中，存在初始样本准确性低、后验概率变化矢量检测精度不理想的问题，结合多元变化检测方法，对基于后验概率变化矢量检测的更新方法进行改进，提出一种可应用于新增建设用地提取的自动化方法。利用两期影像多元变化检测结果提高初始训练样本的准确性，同时在迭代选择样本过程中加入该变化检测结果，改善变化检测更新和重分类过程的精度，更准确地提取新增建设用地。用两期嘉兴地区高分一号影像和前期影像土地利用/覆盖分类数据验证改进效果，并与改进前方法对比。结果表明：改进方法提取的2017年新增建设用地精度更高，提取更新后的2017年建设用地总体精度达到85%，Kappa系数0.7以上，变化检测精度比未改进前显著提高。同时该方法显著减少了迭代次数，提高了提取效率。 关键词： 多元变化检测;  新增建设用地;  自动提取;  后验概率变化矢量检测     Abstract: It is of great significance to extracting new construction land information rapidly and accurately for urbanization monitor research. Because of the low accuracy of original samples in the updating land cover method based on Change Vector Analysis in Posterior Probability Space (CVAPS), and the poor accuracy of change detection of CVAPS, the paper proposed a new automatic approach applied to extract new construction land information effectively. This method was improved from the updating land cover method based on CVAPS by combining with Multivariate Alteration Detection (MAD). The method firstly introduced MAD results of bi-temporal images to improve the accuracy of the initial samples, then added MAD results into the process of iterating samples selection in order to improve the accuracy of change detection and reclassification, thereby extracting new construction land more precisely. A case study of bi-temporal GF-1 images and land use/cover map in Jiaxing area was conducted to test performance of the improved method, and compared this method with CVAPS method. The experimental results show that the new construction land extracted by improved method in 2017 has higher accuracy, its overall accuracy of the updated construction land in 2017 reached 85% and its kappa coefficient is above 0.7. The accuracy of change detection is significantly higher than CVAPS method. Meanwhile, the proposed method reduced number of iteration and raised extraction efficiency significantly.

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