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Mid-season Crop Classification Using Dual-, Compact-, and Full-polarization in Preparation for the Radarsat Constellation Mission (RCM)

机译:中季作物分类使用双重,紧凑型和全极化进行准备雷达星座使命(RCM)

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摘要

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm.
机译:尽管近期对裁剪映射的双(DP)和全极化(FP)合成射线雷达(SAR)数据的潜力进行了研究,但尚未彻底调查紧凑型偏振距(CP)SAR数据的能力。鉴于短暂的雷达坦星座使命(RCM)的可用性,这是特别关注的。以前的研究已经说明了使用DP和FP SAR特征的准确作物映射的潜力,但每个特征对模型精度的贡献是什么都没有得到很好的研究。因此,本研究审查了早期季节(即7月)的潜力,用于加拿大曼尼托巴的农业区作物测绘的Radarsat-2 SAR图像。基于来自FP SAR数据的提取特征来定义各种分类场景,以及两个不同噪声地板的模拟DP和CP SAR数据。使用像素和对象的随机林(RF)分类方案比较总体和单个类别的准确性,与多时间,多极化SAR数据进行比较。 7月底C频段SAR观察是作物映射最有用的数据,但单日图像分类的准确性不足。与SAR反向散射强度特征相比,从CP和FP SAR数据中提取的POARIFETRIC分解特征从CP和FP SAR数据产生相对等于或稍好的分类精度。 RF可变重要性分析显示由于体积散射引起的对去极化敏感的特征是最重要的FP和CP SAR数据。鉴于几种提取的特征高度相关性,协同使用所有特征的协同用途导致了整体分类精度的边际改善。基于集成Spearman相关系数和RF可变重要性分析的基于集成的高度相关特征减少了作物分类的准确性。特别地,使用基于对象的RF算法的FP,CP和DP SAR数据的优化特征,实现了88.23%,82.12%和77.35%的总体精度。

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