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首页> 外文会议>International Conference on Remote Sensing, Environment and Transportation Engineering >Remote Sensing Recognition Winter Wheat with Change Vector Analysis (CVA) Based on Cultivated Parcel Data
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Remote Sensing Recognition Winter Wheat with Change Vector Analysis (CVA) Based on Cultivated Parcel Data

机译：基于栽培包裹数据的遥感识别冬小麦改变载体分析（CVA）

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摘要

The method of CVA is widely used in the studies of the change detection between years, but there were few researches to study the remote sensing recognition crop change between a short time. This paper takes tongzhou, Beijing, as the study area. Winter wheat planting area has been acquired with the method of CVA based on multi-temporal HJ-1-A satellite' CCD data which have a short cycle time. Further more the cultivated parcel data was used as adjust units to correct the measurement result, it has solved the registration error of multi-temporal image at a certain extent and improved the accuracy of winter wheat area measurement. There are some merits about this method. Firstly, the overall pixel accuracy of winter wheat is 91.4% and kappa is 0.801, which is higher than that acquired through post-classification comparison (which are 88.1% and 0.736). Secondly, the method of CVA is more sensitive to the spectrum change. For the growth change of vegetation in different seasons can be detected and the overall accuracy is improved, this approach can be used on the other crop area survey but not only winter wheat. Finally, the cultivated parcel data was used as adjust units to correct the measurement result, it has solved the registration error of multi-temporal image at a certain extent and improved the accuracy of winter wheat area measurement.
机译：CVA的方法广泛应用于多年来变化检测的研究，但少数研究在短时间内研究遥感识别作物变化。本文需要通州，北京，作为研究区。冬季小麦种植区已通过基于多时间HJ-1-A卫星的CCD数据的CVA方法获得，具有短循环时间。此外，更多地使用培养的包裹数据作为调整单元来校正测量结果，它已经在一定程度上解决了多时间图像的登记误差并提高了冬小麦区域测量的准确性。这种方法有一些优点。首先，冬小麦的总像素精度为91.4％，Kappa为0.801，其通过分类后比较获得（88.1％和0.736）。其次，CVA的方法对频谱变化更敏感。对于植被的生长变化，可以检测到不同季节，并且整体准确性得到改善，这种方法可用于其他作物区域调查，但不仅冬小麦。最后，使用培养的包裹数据作为调整单元来校正测量结果，它已经在一定程度上解决了多时间图像的登记误差并提高了冬小麦区域测量的准确性。

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