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Investigation of the effect of the incidence angle on land cover classification using fully polarimetric SAR images

机译:利用全极化SAR图像研究入射角对土地覆盖分类的影响

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摘要

Incidence angle is one of the most important imaging parameters that affect polarimetric SAR (PolSAR) image classification. Several studies have examined the land cover classification capability of PolSAR images with different incidence angles. However, most of these studies provide limited physical insights into the mechanism how the variation of incidence angle affects PolSAR image classification. In the present study, land cover classification was conducted by using RADARSAT-2 Wide Fine Quad-Pol (FQ) images acquired at different incidence angles, namely, FQ8 (27.75 degrees), FQ14 (34.20 degrees), and FQ20 (39.95 degrees). Land cover classification capability was examined for each single-incidence angle image and a multi-incidence angle image (i.e., the combination of single-incidence angle images). The multi-incidence angle image produced better classification results than any of the single-incidence angle images, and the different incidence angles exhibited different superiorities in land cover classification. The effect mechanisms of incidence angle variation on land cover classification were investigated by using the polarimetric decomposition theorem that decomposes radar backscatter into single-bounce scattering, double-bounce scattering and volume scattering. Impinging SAR easily penetrated crops to interact with the soil at a small incidence angle. Therefore, the difference in single-bounce scattering between trees and crops was evident in the FQ8 image, which was determined to be suitable for distinguishing between croplands and forests. The single-bounce scattering from bare lands increased with the decrease in incidence angles, whereas that from water changed slightly with the incidence angle variation. Consequently, the FQ8 image exhibited the largest difference in single-bounce scattering between bare lands and water and produced the fewest confusion between them among all the images. The single- and double-bounce scattering from urban areas and forests increased with the decrease in incidence angles. The increase in single- and double-bounce scattering from urban areas was more significant than that from forests because C-band SAR could not easily penetrate the crown layer of forests to interact with the trunks and ground. Therefore, the FQ8 image showed a slightly better performance than the other images in discriminating between urban areas and forests. Compared with other crops and trees, banana trees caused stronger single- and double-bounce scattering because of their large leaves. As a large incidence angle resulted in a long penetration path of radar waves in the crown layer of vegetation, the FQ20 image enhanced the single- and double-bounce scattering differences between banana trees and other vegetation. Thus, the FQ20 image outperformed the other images in identifying banana trees.
机译:入射角是影响极化SAR(PolSAR)图像分类的最重要的成像参数之一。多项研究检查了具有不同入射角的PolSAR图像的土地覆盖分类能力。但是,大多数这些研究对入射角的变化如何影响PolSAR图像分类的机理提供了有限的物理见解。在本研究中,通过使用在不同入射角(FQ8(27.75度),FQ14(34.20度)和FQ20(39.95度))入射的RADARSAT-2宽精细四极(FQ)图像进行土地覆盖分类。 。针对每个单入射角图像和多入射角图像(即,单入射角图像的组合)检查了土地覆盖分类能力。多入射角图像产生的分类结果比任何单入射角图像都要好,并且不同的入射角在土地覆盖分类中表现出不同的优势。利用极化分解定理研究了入射角变化对土地覆被分类的影响机理,该定理将雷达的反向散射分解为单反射散射,双反射散射和体积散射。撞击SAR很容易穿透农作物,以较小的入射角与土壤相互作用。因此,FQ8图像中树木与农作物之间的单反弹散射差异明显,这被认为适合区分农田和森林。裸地的单反射散射随入射角的减小而增加,而水的单反射散射随入射角的变化而略有变化。因此,FQ8图像在裸露土地和水之间的单跳散射表现出最大差异,并且在所有图像中,它们之间的混淆最少。随着入射角的减小,来自城市地区和森林的单反射和双反射散射增加。由于C波段SAR不能轻易穿透森林的树冠层与树干和地面相互作用,因此城市地区的单跳和双跳散射的增加幅度比森林要大。因此,FQ8图像在区分城市区域和森林方面显示出比其他图像稍好的性能。与其他农作物和树木相比,香蕉树由于叶子大而引起了单跳和双跳的较强散射。由于较大的入射角导致雷达波在植被冠层中的穿透路径较长,因此FQ20图像增强了香蕉树与其他植被之间的单反射和双反射散射差异。因此,FQ20图像在识别香蕉树方面胜过其他图像。

著录项

  • 来源
    《International journal of remote sensing》 |2019年第4期|1576-1593|共18页
  • 作者单位

    Sun Yat Sen Univ, Guangdong Prov Key Lab Urbanizat & Geosimulat, Sch Geog & Planning, Guangzhou, Guangdong, Peoples R China;

    Sun Yat Sen Univ, Guangdong Prov Key Lab Urbanizat & Geosimulat, Sch Geog & Planning, Guangzhou, Guangdong, Peoples R China;

    East China Normal Univ, Sch Geog Sci, Key Lab Geog Informat Sci, Minist Educ, Shanghai, Peoples R China;

    Univ Hong Kong, Dept Urban Planning & Design, Hong Kong, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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