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Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon

机译:巴西亚马逊土地和土地覆盖图的光学和雷达数据集成

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This study aims to evaluate different methods of integrating optical and multipolarized radar data for land use and land cover (LULC) mapping in an agricultural frontier region in the Central Brazilian Amazon, which requires continuous monitoring due to the increasing human intervention. The evaluation is performed using different sets of fused and combined data. This article also proposes to apply the principal component (PC) technique to the multipolarized synthetic aperture radar (SAR), prior to the optical and radar data PC fusion process, aiming at the use of all available polarized information in the fusion process. Although the fused images improve the visual interpretation of the land use classes, the best results are achieved with the simple combination of the Advanced Land Observing Satellite (ALOS)/phased array L-Band SAR (PALSAR) with the LANDSAT5/Thematic Mapper (TM) images. Radar information is found to be particularly useful for improving the user accuracies (UAs) of Soybean with 40 days after seeding (an increase of about 55%), Dirty Pasture (22%), Degraded Forest and Regeneration (5%), and the producer accuracies (PAs) of Clean Pasture (39%), Fallow Agriculture (16%), Degraded Forest and Regeneration (3%), and Primary Forest (2%). Information from the HH (horizontal transmit and horizontal receive) polarization contributes more than that from HV (horizontal transmit and vertical receive) polarization to discriminate the classes, although the use of both polarizations produces results that are statistically better than those obtained with a single polarization.
机译:这项研究旨在评估在巴西中部亚马逊地区一个农业边境地区整合光学和多极化雷达数据以用于土地利用和土地覆被(LULC)制图的不同方法,由于人为干预的增加,需要对其进行连续监测。使用不同的融合和组合数据集执行评估。本文还建议在光学和雷达数据PC融合过程之前,将主成分(PC)技术应用于多极化合成孔径雷达(SAR),旨在在融合过程中使用所有可用的极化信息。尽管融合后的图像改善了土地使用类别的视觉解释,但通过将高级陆地观测卫星(ALOS)/相控阵L波段SAR(PALSAR)与LANDSAT5 / Thematic Mapper(TM)进行简单组合,可以获得最佳结果) 图片。已发现雷达信息对于提高播种后40天(增加约55%),肮脏牧场(22%),森林退化和更新(5%)以及大豆使用后40天的大豆使用者准确性(UAs)特别有用。清洁牧场(39%),休闲农业(16%),退化森林与更新(3%)和原始森林(2%)的生产者精确度(PA)。来自HH(水平发射和水平接收)极化的信息比来自HV(水平发射和垂直接收)极化的信息对区分类别的贡献更大,尽管使用两种极化产生的结果在统计上都比单极化得到的结果更好。

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