Theoretical Characterization of X-Band Multiincidence Angle and Multipolarimetric SAR Data From Rice Paddies at Late Vegetative Stage

Arii, Motofumi; Yamada, Hiroyoshi; Kobayashi, Tatsuharu; Kojima, Soichi; Umehara, Toshihiko; Komatsu, Tomomi; Nishimura, Takeshi

doi:10.1109/tgrs.2017.2652447

Cited by 23 publications

(15 citation statements)

References 31 publications

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“…Cosine squared distribution sits in between these two. The randomness is defined by σ as shown in the cosine squared distribution [32].…”

Section: Model-based Polarimetric Decompositions and Their Limitationmentioning

confidence: 99%

“…In [32], Arii et al have first applied the model-based polarimetric decomposition technique to Figure 8. C remainder of ANNED (left) for the L-band Black Forest image compared with those of NNED' (dash) using two distributions: cosine squared (center) and uniform (right) distribution.…”

Section: Model-based Polarimetric Decompositions and Their Limitationmentioning

confidence: 99%

“…In [32], Arii et al have first applied the model-based polarimetric decomposition technique to real multi-incidence angle and multi-polarimetric SAR (MIMP SAR) data sets from rice paddies at the X-band obtained by Pi-SAR2 developed by the National Institute of Information and Communications Technology. Contribution of each scattering mechanism derived by the Freeman and Durden algorithm is shown in Figure 9.…”

Section: Model-based Polarimetric Decompositions and Their Limitationmentioning

confidence: 99%

“…Forward models generally have a high degree of freedom so that many parameters must be reasonably determined. For this purpose, a multiparametric SAR observation, where various observation conditions such as frequency, polarization and incidence angle are gradually varied in a wide range of each value during a short span of time, has been proposed [27][28][29][30][31][32][33][34][35][36]. Simulation parameters can be found by fitting the model to the series of data set obtained by the multiparametric SAR observation.…”

Section: Introductionmentioning

confidence: 99%

“…Then, forward models are reviewed with real SAR data based on a multiparametric SAR observation. Note that the main part of this review article is based on our previous works [18,19,21,32,34,36,37].…”

Section: Introductionmentioning

confidence: 99%

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Review of the Comprehensive SAR Approach to Identify Scattering Mechanisms of Radar Backscatter from Vegetated Terrain

et al. 2019

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In a field of polarimetric synthetic aperture radar (SAR) remote sensing, various kinds of polarimetric decomposition techniques have been proposed. However, poor validations prevent them from operational applications. A true composition ratio of scattering mechanisms within a radar backscatter plays a key role. To overcome the issue, a novel comprehensive SAR approach to accurately identify a contribution of each scattering mechanism has been introduced. This is based on multiparametric SAR observation combined with a numerical model simulation. In this article, a comprehensive SAR approach is concisely reviewed to accelerate the research in this field. First, popular model-based polarimetric decompositions are introduced and their limitations are shown. Then, a behavior of scattering mechanisms is analyzed by the discrete scatterer model with some results using real multiparametric SAR data. A comprehensive SAR approach must be essential to realize an operational use of polarimetric SAR data.

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“…Cosine squared distribution sits in between these two. The randomness is defined by σ as shown in the cosine squared distribution [32].…”

Section: Model-based Polarimetric Decompositions and Their Limitationmentioning

confidence: 99%

Section: Model-based Polarimetric Decompositions and Their Limitationmentioning

confidence: 99%

Section: Model-based Polarimetric Decompositions and Their Limitationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Review of the Comprehensive SAR Approach to Identify Scattering Mechanisms of Radar Backscatter from Vegetated Terrain

et al. 2019

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A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets

Gao

Wang

et al. 2021

Remote Sensing of Environment

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Terrain effect analysis and compensation method for polarimetric synthetic aperture radar interferometry height parameter inversion

Suo

Wang

Xue

et al. 2022

IET Radar Sonar & Navi

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In this paper, the terrain effect on parameter inversion performance of polarimetric synthetic aperture radar (PolSAR) and polarimetric SAR interferometry (PolInSAR) are analysed first. Based on the analysis of the terrain effect on single pixel processing, the terrain effect between adjacent pixels is addressed for both PolSAR and PolInSAR image pairs. Through the analysis, the terrain effect should be considered not only for the single pixel data calibration but also for the adjacent pixels of PolSAR and PolInSAR parameter inversion processing. Therefore, combining with the geometric model correction of sloped‐random volume over ground, the terrain‐compensated procedure is proposed for PolInSAR parameter inversion. The parameter inversion performance is improved effectively with terrain effect consideration. The effectiveness of the proposed method is investigated by the PolSARPro simulated dataset and the real Phased Array L‐Band Synthetic aperture Radar (PALSAR) dataset.

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Theoretical Characterization of X-Band Multiincidence Angle and Multipolarimetric SAR Data From Rice Paddies at Late Vegetative Stage

Cited by 23 publications

References 31 publications

Review of the Comprehensive SAR Approach to Identify Scattering Mechanisms of Radar Backscatter from Vegetated Terrain

Review of the Comprehensive SAR Approach to Identify Scattering Mechanisms of Radar Backscatter from Vegetated Terrain

A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets

Terrain effect analysis and compensation method for polarimetric synthetic aperture radar interferometry height parameter inversion

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