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

机译:使用多日envisat-1 ASAR数据的稻米作物库存处理方法

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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:由于其独立于云覆盖,预计将成为热带和亚热带地区的农业应用的主要高分辨率遥感数据源的主导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,其能力包括用于获取具有不同入射角的图像的光束转向,决斗偏振和宽宽度覆盖。基于遥感数据的农业作物库存将通过ASAR的新功能来提高。在本文中,已经使用多日期ASAR数据进行了用于稻米作物库存的过程。该过程包括两部分:稻田多日期数据的数据预处理和分类。为了开展研究,使用了富州地区2004年覆盖福州地区的6种ASAR图像场景。 PCI 9.1用于数据预处理,包括数据校准,图像共同登记,散斑抑制,矫形和幅度到DB转换。在该过程中应用一些新方法,例如用于图像共同登记的相关匹配和用于散斑抑制的多通道滤波。使用面向对象的分类器,与K-Meansived分类器和最大似然分类器相比,实现了更高的分类准确性。通过采用本文提出的程序,福州市达到了90%以上的稻米分类准确性,具有多日常envisat ASAR数据。这表明使用多日ASAR数据的稻米作物库存是可行的。

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