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A processing method for rice crop inventory using multi-date ENVISAT-1 ASAR data

机译:使用多日期ENVISAT-1 ASAR数据的水稻作物库存处理方法

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Synthetic Aperture Radar (SAR) is anticipated to be the dominant high-resolution remote sensing data source for agricultural applications in tropical and subtropical regions due to its independent from cloud cover. ENVISAT-1 ASAR is the most advanced satellite radar-imaging instrument, its capabilities include beam steering for acquiring images with different incidence angles, duel polarization and wide swath coverage. Agricultural crop inventory based on remote sensed data will be improved greatly by ASAR's new capabilities. In this paper, a procedure has been developed using multi-date ASAR data for rice crop inventory. The procedure comprises two parts: data preprocessing and classification of multi-date data for rice field. In order to carry out the research, 6 scenes of ASAR images covering Fuzhou area year round of 2004 were used. PCI 9.1 is used for data preprocessing which includes data calibration, image co-registration, speckle suppression, orthorecitification and amplitude-to-dB conversion. Some novel methods are applied in this procedure such as correlation matching for image co-registration and multi-channel filtering for speckle suppression. Object-oriented classifier was used, compared with K-means supervised classifier and maximum likelihood classifier, and higher classification accuracy was achieved. By adopting the procedure presented in this paper, more than 90% classification accuracy for rice was achieved in Fuzhou city with multi-date Envisat ASAR data. This indicates that the procedure is feasible for rice crop inventory using multi-date ASAR data.
机译:合成孔径雷达(SAR)由于独立于云层,因此有望成为热带和亚热带地区农业应用的主要高分辨率遥感数据源。 ENVISAT-1 ASAR是最先进的卫星雷达成像仪器,其功能包括光束转向以获取具有不同入射角,双极化和宽幅覆盖范围的图像。 ASAR的新功能将大大改善基于遥感数据的农作物清单。在本文中,已经使用多日期ASAR数据开发了水稻作物库存的程序。该程序包括两个部分:数据预处理和稻田多日期数据的分类。为了进行这项研究,使用了2004年全年覆盖福州地区的6个ASAR图像场景。 PCI 9.1用于数据预处理,其中包括数据校准,图像共配准,散斑抑制,正则化和幅度-dB转换。此过程中应用了一些新颖的方法,例如用于图像共配准的相关匹配和用于斑点抑制的多通道过滤。与K-means监督分类器和最大似然分类器相比,使用了面向对象分类器,分类精度更高。通过采用本文提出的程序,使用多日期的Envisat ASAR数据在福州市实现了90%以上的大米分类精度。这表明该程序对于使用多日期ASAR数据的水稻作物清单是可行的。

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