Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping
Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping
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DOI：
10.3390/rs11161920
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发表时间：
2019-08
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影响因子：
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通讯作者：
Rei Sonobe
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文献类型：
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作者：
Rei Sonobe
搭载X波段合成孔径雷达(XSAR)的具有新系统架构的高级观测卫星2号(ASNARO-2)于2018年1月17日发射升空，预计将用于补充更大卫星提供的数据。土地覆盖分类是遥感最常见的应用之一，其结果为农田管理和估计潜在收成提供了可靠的资源。本文介绍了ASNARO-2 XSAR数据在农业作物分类中的初步试验结果。在以前的研究中，Sentinel-1C-SAR数据已被广泛用于识别作物类型。利用2018年6月和8月获得的数据，对ASNARO-2 XSAR和Sentinel-1 C-SAR进行了比较，以识别五种作物类型(豆类、甜菜根、玉米、马铃薯和冬小麦)，并对这些数据的组合进行了测试。为了评估对作物进行准确分类的可能性，根据两个日期的后向散射系数计算了一些雷达植被指数。此外，使用四种常用的监督学习模型：支持向量机(SVM)、随机森林(RF)、多层前馈神经网络(FNN)和基于核的极端学习机(KELM)，对每种类型的SAR数据的潜力进行了评估。ASNARO-2XSAR和Sentinel-1C-SAR数据的结合是有效的，利用支持向量机获得了85.4±1.8%的分类精度。
The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 ± 1.8% were achieved using SVM.