• 대한원격탐사학회지
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• 제23권5호
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• pp.455-464
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• 2007

Lee(2007) suggested the Point-Jacobian iteration MAP estimation(PJIMAP) for noise removal of the images that are corrupted by multiplicative speckle noise. It is to find a MAP estimation of noisy-free imagery based on a Bayesian model using the lognormal distribution for image intensity and an MRF for image texture. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. In this study, the MAP estimation is computed by the Point-Jacobian iteration using adaptive parameters. At each iteration, the parameters related to the Bayesian model are adaptively estimated using the updated information. The results of the proposed scheme were compared to them of PJIMAP with SAR simulation data generated by the Monte Carlo method. The experiments demonstrated an improvement in relaxing speckle noise and estimating noise-free intensity by using the adaptive parameters for the Ponit-Jacobian iteration.

• 대한원격탐사학회지
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• 제30권6호
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• pp.817-827
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• 2014

위성 원격 탐사에서는 센서 운영 환경으로 인하여 영상을 수집하는 동안 영상의 질 저하가 일어나며 이러한 영상의 질 저하는 관측된 자료로부터 유용한 정보를 확인하거나 추출하는 데 악 영향을 미치는 번짐 현상(blurring)과 잡음 (noise)을 야기시킨다. 본 연구는 원격 탐사 영상 자료의 질 저하 현상을 모형화하기 위해 Gaussian 가산 잡음과 Markov random field로 정의되는 공간적 연결성을 가정하였다. 그리고 질 저하된 관측 자료로부터 원래 강도의 영상을 복원하기 위한 Point-Jacobian 반복 maximum a posteriori (MAP) 추정 법을 제안한다. 제안 연구는 이웃 창의 형태로 8 개 방향의 창으로 구성된 방사형을 사용하며 각 방향에서의 중심 화소와의 이웃 화소들 간의 Mahalanobis 제곱 거리를 경계 근접성 측정치로 사용한다. 제안 방법의 성능을 평가하기 위해서 고해상도 영상 자료에 나타날 수 있는 다양한 형태의 패턴을 사용하는 simulation 자료를 생성하여 화소 단위 분류 법을 사용하여 정량적 평가를 수행하였고 한반도 안양 북부 지역에서 관측된 1 m 급 IKONOS 자료의 무감독 분할을 통해 정성적 평가를 수행하였다. 실험 결과는 고해상도 원격 탐사 자료 분석에서 제안 영상 복원 법을 적용하면 현저히 분석의 정확성을 높이는 것을 보여 준다.

• 대한원격탐사학회지
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• 제25권5호
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• pp.455-464
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• 2009

Lee(2009)에서 영상 강도를 위해서 lognormal 확률 모형과 영상 texture를 위해서 Markov random field(MRF)에 기반하는 Bayesian 모형을 사용하는 boundary-adaptive despeckling 방법을 제안하였다. 이 방법은 speckle 제거 영상의 최대 사후(maximum a posteriori: MAP) 추정치를 구하기 위해서 Point-Jacobian iteration을 이용한다 인접하고 있는 다른 특성의 지역에 위치한 화소의 값을 사용하는 가능성을 줄이기 위해 Boundary-adaptive algorithm은 경계에 가까울 수록 멀리 떨어진 이웃 화소로부터 정보를 덜 수집하도록 고안된다. 이러한 boundary-adaptive 방법은 전반적으로 simulation 자료를 사용하여 Lee(2009)에서 평가되었고 그리고 제안된 방법의 효험을 증명하였다. 본 연구는 Lee(2009)의 확장 연구로 MAP 추정치를 구하기 반복 algorithm의 계산 효율성을 증가 시키고 noise 제거와 함께 분류를 수행하는 수정 algorithm을 제안한다. Simulation 자료를 사용한 실험을 통해서 boundary-adaption이 분류 오류를 줄여줄 뿐 아니라 더욱 명확한 경계선을 보여준다는 것을 알 수 있다. 또한 영종도 서해안에서 관측된 고해상도 Terra-SAR data에 적용한 결과는 boundary-adaption은 SAR 활용에서 분석의 정확성을 개선 시킬 수 있다는 것을 암시한다.

• 대한원격탐사학회지
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• 제23권1호
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• pp.33-42
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• 2007

In this paper, an iterative MAP approach using a Bayesian model based on the lognormal distribution for image intensity and a GRF for image texture is proposed for despeckling the SAR images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. MRFs have been used to model spatially correlated and signal-dependent phenomena for SAR speckled images. The MRF is incorporated into digital image analysis by viewing pixel types as slates of molecules in a lattice-like physical system defined on a GRF Because of the MRF-SRF equivalence, the assignment of an energy function to the physical system determines its Gibbs measure, which is used to model molecular interactions. The proposed Point-Jacobian Iterative MAP estimation method was first evaluated using simulation data generated by the Monte Carlo method. The methodology was then applied to data acquired by the ESA's ERS satellite on Nonsan area of Korean Peninsula. In the extensive experiments of this study, The proposed method demonstrated the capability to relax speckle noise and estimate noise-free intensity.

• 대한원격탐사학회지
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• 제25권3호
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• pp.295-309
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• 2009

In this study, an iterative maximum a posteriori (MAP) approach using a Bayesian model of Markovrandom field (MRF) was proposed for despeckling images that contains speckle. Image process is assumed to combine the random fields associated with the observed intensity process and the image texture process respectively. The objective measure for determining the optimal restoration of this "double compound stochastic" image process is based on Bayes' theorem, and the MAP estimation employs the Point-Jacobian iteration to obtain the optimal solution. In the proposed algorithm, MRF is used to quantify the spatial interaction probabilistically, that is, to provide a type of prior information on the image texture and the neighbor window of any size is defined for contextual information on a local region. However, the window of a certain size would result in using wrong information for the estimation from adjacent regions with different characteristics at the pixels close to or on boundary. To overcome this problem, the new method is designed to use less information from more distant neighbors as the pixel is closer to boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The proximity to boundary is estimated using a non-uniformity measurement based on standard deviation of local region. The new scheme has been extensively evaluated using simulation data, and the experimental results show a considerable improvement in despeckling the images that contain speckle.

• 대한원격탐사학회지
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• 제24권1호
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• pp.79-89
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• 2008

Irregular temporal sampling is a common feature of geophysical and biological time series in remote sensing. This study proposes an on-line system for reconstructing observation image series contaminated by noises resulted from mechanical problems or sensing environmental condition. There is also a high likelihood that during the data acquisition periods the target site corresponding to any given pixel may be covered by fog or cloud, thereby resulting in bad or missing observation. The surface parameters associated with the land are usually dependent on the climate, and many physical processes that are displayed in the image sensed from the land then exhibit temporal variation with seasonal periodicity. A feedback system proposed in this study reconstructs a sequence of images remotely sensed from the land surface having the physical processes with seasonal periodicity. The harmonic model is used to track seasonal variation through time, and a Gibbs random field (GRF) is used to represent the spatial dependency of digital image processes. The experimental results of this simulation study show the potentiality of the proposed system to reconstruct the image series observed by imperfect sensing technology from the environment which are frequently influenced by bad weather. This study provides fundamental information on the elements of the proposed system for right usage in application.

• 대한원격탐사학회지
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• 제24권5호
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• pp.525-533
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• 2008

This study presents a pansharpening method, so called FitPAN, to synthesize multispectral images at a higher resolution by exploiting a high-resolution image acquired in panchromatic modality. FitPAN is a modified version of the quadratic programming approach proposed in (Lee, 2008), which is designed to generate synthesized multispectral images similar to the multispectral images that would have been observed by the corresponding sensor at the same high resolution. The proposed scheme aims at reconstructing the multispectral images at the higher resolution with as less spectral distortion as possible. This study also proposes a sharpening process to eliminate some distortions appeared in the fused image of the higher resolution. It employs the Point-Jacobian MAP iteration utilizing the contextual information of the original panchromatic image. In this study, the new method was applied to the IKONOS 1m panchromatic and 4m multispectral data, and the results were compared with them of several current approaches. Experimental results demonstrate that the proposed scheme can achieve significant improvement in both spectral and block distortion.

• 한국경영과학회지
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• 제12권1호
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• pp.10-18
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• 1987

This paper discusses algorithmic properties of a class of complementarity programs involving strictly diagonally isotone and off-diagonally isotone functions, i. e., functions whose Jacobian matrices have positive diagonal elements and nonnegative off-diagonal elements, A typical traffic equilibrium under elastic demands is cast into this class. Algorithmic properties of these complementarity problems, when a Jacobi-type iteration is applied, are investigated. It is shown that with a properly chosen starting point the generated sequence are decomposed into two converging monotonic subsequences. This and related will be useful in developing solution procedures for this class of complementarity problems.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.270-273
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• 2007

In this paper, a SAR-despeck1ing approach of adaptive iteration based a Bayesian model using the lognormal distribution for image intensity and a Gibbs random field (GRF) for image texture is proposed for noise removal of the images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. The iterative approach based on MRF is very effective for the inner areas of regions in the observed scene, but may result in yielding false reconstruction around the boundaries due to using wrong information of adjacent regions with different characteristics. The proposed method suggests an adaptive approach using variable parameters depending on the location of reconstructed area, that is, how near to the boundary. The proximity of boundary is estimated by the statistics based on edge value, standard deviation, entropy, and the 4th moment of intensity distribution.

• 대한수학회보
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• 제26권2호
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• pp.127-134
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• 1989

This paper proposes a deformation method for solving practical nonlinear programming problems. Utilizing the nonlinear parametric programming technique with Quasi-Newton method [6,7], the method solves the problem by imbedding it into a suitable one-parameter family of problems. The approach discussed in this paper was originally developed with the aim of solving a system of structural optimization problems with frequently appears in various kind of engineering design. It is assumed that we have to solve more than one structural problem of the same type. It an optimal solution of one of these problems is available, then the optimal solutions of thel other problems can be easily obtained by using this known problem and its optimal solution as the initial problem of our parametric method. The method of nonlinear programming does not generally converge to the optimal solution from an arbitrary starting point if the initial estimate is not sufficiently close to the solution. On the other hand, the deformation method described in this paper is advantageous in that it is likely to obtain the optimal solution every if the initial point is not necessarily in a small neighborhood of the solution. the Jacobian matrix of the iteration formula has the special structural features [2, 3]. Sectioon 2 describes nonlinear parametric programming problem imbeded into a one-parameter family of problems. In Section 3 the iteration formulas for one-parameter are developed. Section 4 discusses parametric approach for Quasi-Newton method and gives algorithm for finding the optimal solution.