Incidence Angle Correction of SAR Sea Ice Data Based on Locally Linear Mapping

Lang, Wenhui; Pan, Zhiyong; Jie, Wu; Yang, Shiguang; Yang, Xiu

doi:10.1109/tgrs.2015.2513159

Cited by 21 publications

(8 citation statements)

References 31 publications

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“…where θ i is the incidence angle,Ī 0 is the mean intensity at θ = 0 and c is a surface-specific constant. The exponential decay of intensities implies the linear decay of log-intensities, a practical simplification used in recent studies ( [7], [11], [22], [9]):Ī…”

Section: A Modeling the Incidence Angle Effectmentioning

confidence: 99%

“…The empirically observed average decay rates have sometimes been approximated as linear in decibels per degree ([dB/1 • ]) ( [7], [8]). Based on simple modeling and decay rate measurements, global correction ( [9], [10]) and manual class-based correction methods ( [11], [2], [12]) have become the standard mitigation approaches. These approaches, although useful, have their limitations.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images

Cristea

Houtte

Doulgeris

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic Aperture Radar (SAR) systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Due to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wideswath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth, systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2 and UAVSAR wide-swath scenes containing open water, sea ice and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments.

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Section: A Modeling the Incidence Angle Effectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images

Cristea

Houtte

Doulgeris

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…One of them is direct slope estimation from backscatter intensity versus incidence angle plots. Another one is, the determination of the σ° versus θ 0 dependence are based on SAR image pairs acquired on ascending and descending orbits over the same sea ice area with a short time difference (Aldenhoff et al 2020) and then incidence angle normalization is regularly applied for various image classification tasks, e.g., (Lang et al 2016;Zakhvatkina et al 2017;Murashkin et al 2018). In the context of this study, the influence of various incidence angles on backscatter intensity in TanDEM-X mission is not possible to assess, as our analysis is based on one image with relatively narrow swath (42.33-44.46).…”

Section: Effects Of Image Acquisition Geometry On Ice Type Classificamentioning

confidence: 99%

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

Marbouti

Antropov

Praks

et al. 2020

Geo-spatial Information Science

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“…Microwave-based sea ice remote sensing is mainly detected by synthetic-aperture radar (SAR). 3 Although real-time imaging can be guaranteed, the cost required for large-scale sea ice monitoring is high.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of sea ice thickness using global navigation satellite system reflected signals

Gao

Yang

Wang

2019

Measurement and Control

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The aim of this paper is to develop a model which can be used to retrieve sea ice thickness based on global navigation satellite system reflected signals at a shore-based platform. First, the method calculates the intensity ratio of the reflected signal and the direct signal of the global navigation satellite system satellite, which is the ratio of the power of the reflected signal to the power of the direct signal. Then, the information of the sea ice thickness is obtained according to the empirical model of the sea ice thickness. In order to verify the effectiveness of the method, the global navigation satellite system reflected signals were observed in the experiment in the Bayu enclosure of Liaoning Province, China. The results show that the sea ice thickness of the global navigation satellite system reflected signal is 10–20 cm, which is consistent with the synthetic-aperture radar observation.

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Incidence Angle Correction of SAR Sea Ice Data Based on Locally Linear Mapping

Cited by 21 publications

References 31 publications

Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images

Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

Retrieval of sea ice thickness using global navigation satellite system reflected signals

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