Generating and correcting rational polynomial coefficients using image correction model

Namhoon Kim; Hyo Seon Jang; Mohammad Gholami Farkoushi; Yoon Jo Choi; Hong Gyoo Sohn

Generating and correcting rational polynomial coefficients using image correction model

Namhoon Kim, Hyo Seon Jang, Mohammad Gholami Farkoushi, Yoon Jo Choi, Hong Gyoo Sohn

Department of Civil and Environmental Engineering

Research output: Contribution to conference › Paper › peer-review

Abstract

We propose a method to regenerate and correct Rational Polynomial Coefficients (RPCs) using KOMPSAT-3A imagery. Physical sensor model of KOMPSAT-3A and virtual grid over the target area were used to estimate the new RPCs. Three different image correction models (image coordinate translation model, shift and drift model, and affine transformation model) were used to remove image space error. We tested our method in Seoul and Goheung area. When using un-corrected new RPCs, the results showed a 20-30 pixel error. After applying image correction models, the error reduced to 1.8 to 5.5 pixels. The expected error map was generated from the error propagation analysis using Gauss-Markov Model.

Original language	English
Publication status	Published - 2020 Jan 1
Event	40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019 - Daejeon, Korea, Republic of Duration: 2019 Oct 14 → 2019 Oct 18

Conference

Conference	40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019
Country/Territory	Korea, Republic of
City	Daejeon
Period	19/10/14 → 19/10/18

All Science Journal Classification (ASJC) codes

Information Systems

Cite this

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title = "Generating and correcting rational polynomial coefficients using image correction model",

abstract = "We propose a method to regenerate and correct Rational Polynomial Coefficients (RPCs) using KOMPSAT-3A imagery. Physical sensor model of KOMPSAT-3A and virtual grid over the target area were used to estimate the new RPCs. Three different image correction models (image coordinate translation model, shift and drift model, and affine transformation model) were used to remove image space error. We tested our method in Seoul and Goheung area. When using un-corrected new RPCs, the results showed a 20-30 pixel error. After applying image correction models, the error reduced to 1.8 to 5.5 pixels. The expected error map was generated from the error propagation analysis using Gauss-Markov Model.",

author = "Namhoon Kim and Jang, {Hyo Seon} and Farkoushi, {Mohammad Gholami} and Choi, {Yoon Jo} and Sohn, {Hong Gyoo}",

year = "2020",

month = jan,

day = "1",

language = "English",

note = "40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019 ; Conference date: 14-10-2019 Through 18-10-2019",

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Generating and correcting rational polynomial coefficients using image correction model. / Kim, Namhoon; Jang, Hyo Seon; Farkoushi, Mohammad Gholami et al.
2020. Paper presented at 40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019, Daejeon, Korea, Republic of.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Generating and correcting rational polynomial coefficients using image correction model

AU - Kim, Namhoon

AU - Jang, Hyo Seon

AU - Farkoushi, Mohammad Gholami

AU - Choi, Yoon Jo

AU - Sohn, Hong Gyoo

PY - 2020/1/1

Y1 - 2020/1/1

N2 - We propose a method to regenerate and correct Rational Polynomial Coefficients (RPCs) using KOMPSAT-3A imagery. Physical sensor model of KOMPSAT-3A and virtual grid over the target area were used to estimate the new RPCs. Three different image correction models (image coordinate translation model, shift and drift model, and affine transformation model) were used to remove image space error. We tested our method in Seoul and Goheung area. When using un-corrected new RPCs, the results showed a 20-30 pixel error. After applying image correction models, the error reduced to 1.8 to 5.5 pixels. The expected error map was generated from the error propagation analysis using Gauss-Markov Model.

AB - We propose a method to regenerate and correct Rational Polynomial Coefficients (RPCs) using KOMPSAT-3A imagery. Physical sensor model of KOMPSAT-3A and virtual grid over the target area were used to estimate the new RPCs. Three different image correction models (image coordinate translation model, shift and drift model, and affine transformation model) were used to remove image space error. We tested our method in Seoul and Goheung area. When using un-corrected new RPCs, the results showed a 20-30 pixel error. After applying image correction models, the error reduced to 1.8 to 5.5 pixels. The expected error map was generated from the error propagation analysis using Gauss-Markov Model.

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M3 - Paper

T2 - 40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019

Y2 - 14 October 2019 through 18 October 2019

ER -

Generating and correcting rational polynomial coefficients using image correction model

Abstract

Conference

All Science Journal Classification (ASJC) codes

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