• Current Optics and Photonics
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• v.5 no.4
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• pp.431-443
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• 2021

Sparse unmixing has been proven to be an effective method for hyperspectral unmixing. Hyperspectral images contain rich spectral and spatial information. The means to make full use of spectral information, spatial information, and enhanced sparsity constraints are the main research directions to improve the accuracy of sparse unmixing. However, many algorithms only focus on one or two of these factors, because it is difficult to construct an unmixing model that considers all three factors. To address this issue, a novel algorithm called multiview-based spectral weighted and low-rank row-sparsity unmixing is proposed. A multiview data set is generated through spectral partitioning, and then spectral weighting is imposed on it to exploit the abundant spectral information. The row-sparsity approach, which controls the sparsity by the l_2,0 norm, outperforms the single-sparsity approach in many scenarios. Many algorithms use convex relaxation methods to solve the l_2,0 norm to avoid the NP-hard problem, but this will reduce sparsity and unmixing accuracy. In this paper, a row-hard-threshold function is introduced to solve the l_2,0 norm directly, which guarantees the sparsity of the results. The high spatial correlation of hyperspectral images is associated with low column rank; therefore, the low-rank constraint is adopted to utilize spatial information. Experiments with simulated and real data prove that the proposed algorithm can obtain better unmixing results.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.820-824
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• 2003

One of the most important research areas on remote sensing is spectral unmixing of hyper-spectral data. For spectral unmixing of hyper spectral data, accurate land cover information is necessary. But obtaining accurate land cover information is difficult process. Obtaining land cover information from high-resolution data may be a useful solution. In this study spectral signature of endmembers on ASTER acquired in October was calculated from land cover information on IKONOS acquired in September. Then the spectral signature of endmembers applied to ASTER images acquired on January and March. Then the result of spectral unmxing of them evauateted. The spectral signatures of endmembers could be applied to different seasonal images. When it applied to an ASTER image which have similar zenith angle to the image of the spectral signatures of endmembers, spectral unmixing result was reliable. Although test data has different zenith angle from the image of spectral signatures of endmembers, the spectral unmixing results of urban and vegetation were reliable.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.211-216
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• 2006

The Constrained Linear Spectral Unmixing(CLSU) is investigated for sub-pixel image processing, Its result is the abundance map which mean fractions of endmember existing in a mixed pixel. Compared to the Linear Spectral Unmixing using least square method, CLSU uses the NNLS (Non-Negative Least Square) algorithm to guarantee that the estimated fractions are constrained. But, CLSU gets Into difficulty in image processing due to select endmember at a user's disposition. In this study, endmember selection optimization method using entropy in the error-image analysis is proposed. In experiments which is used hyperion image, it is shown that our method can select endmember number than CLSU based on unsupervised endemeber selection.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.161-166
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• 2003

Hyperspectral imagery have high spectral resolution and provide the potential for more accurate and detailed information extraction than any other type of remotely sensed data. In this paper, the "Linear Spectral Unmixing" model which is one solution to overcome the limit of spatial resolution for remote sensing data was introduced and we applied the algorithm to hyperspectral image. The result was not good because of some problems such as image calibration and used endmembers. Therefore, we analyzed the cause and had a search for a solution.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.205-205
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• 2004

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.361-370
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• 2015

Various algorithms such as Chronochrome(CC), Principle Component Analysis(PCA), and spectral unmixing have been studied for hyperspectral change detection. Change detection by spectral unmixing offers useful information on the nature of the change compared to the other change detection methods which provide only the locations of changes in the scene. However, hyperspectral change detection by spectral unmixing is still in an early stage. This research proposed a new approach to extract endmembers, which have identical properties in temporally different images, by Iterative Error Analysis (IEA) and Spectral Angle Mapper(SAM). The change map obtained from the difference of abundance efficiently showed the changed pixels. Simulated images generated from Compact Airborne Spectrographic Imager (CASI) and Hyperion were used for change detection, and the experimental results showed that the proposed method performed better than CC, PCA, and spectral unmixing using N-FINDR. The proposed method has the advantage of automatically extracting endmembers without prior information, and it could be applicable for the real images composed of many materials.

• Journal of Theoretical and Applied Mechanics
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• v.3 no.1
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• pp.45-65
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• 2002

A constitutive model on oorthotropic thermo-elasto-viscoplasticity for fiber-reinforced composite materials Is illustrated, and their thermomechanical responses are predicted with the fully-coupled finite element formulation. The unmixing-mixing scheme can be adopted with the multipartite matrix method as the constitutive model. Basic assumptions based upon the composite micromechanics are postulated, and the strain components of thermal expansion due to temperature change are included In the formulation. Also. more than two sets of mechanical variables, which represent the deformation states of multipartite matrix can be introduced arbitrarily. In particular, the unmixing-mixing scheme can be used with any well-known isotropic viscoplastic theory of the matrix material. The scheme unnecessitates the complex processes for developing an orthotropic viscoplastic theory. The governing equations based on fully-coupled thermomechanics are derived with constitutive arrangement by the unmixing-mixing concept. By considering some auxiliary conditions, the Initial-boundary value problem Is completely set up. As a tool of numerical analyses, the finite element method Is used with isoparametric Interpolation fer the displacement and the temperature fields. The equation of mutton and the energy conservation equation are spatially discretized, and then the time marching techniques such as the Newmark method and the Crank-Nicolson technique are applied. To solve the ultimate nonlinear simultaneous equations, a successive iteration algorithm is constructed with subincrementing technique. As a numerical study, a series of analyses are performed with the main focus on the thermomechanical coupling effect in composite materials. The progress of viscoplastic deformation, the stress-strain relation, and the temperature History are careful1y examined when composite laminates are subjected to repeated cyclic loading.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.2
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• pp.91-97
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• 2013

Pyrolysis and surface recession of charring composites are progressed primarily in the thickness direction. The unmixing-mixing scheme is applied to describe the in-plane and through-thickness behaviors of porous composites. The extended unmixing-mixing equations are based on transverse isotropy of unidirectionally fiber-reinforced composites. The strain components of gas pressure in pores, thermal expansion, and chemical shrinkage are included in the constitutive model. By analyzing micromechanical representative volume elements of porous composites, the validity of the derived equations are examined.

• Composites Research
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• v.25 no.2
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• pp.34-39
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• 2012

A thermo-poro-elastic constitutive model of unidirectionally fiber-reinforced composite materials is suggested by extending the unmixing-mixing scheme which is based upon composite micromechanics. The strain components of thermal expansion due to a temperature change, gas pressure in pores, and chemical shrinkage are included in the constitutive model. On purpose to verify the derived constitutive relations, the representative volume element of two-dimensional lamina subject to various loading conditions is analyzed by the finite element method. The overall stress and strain responses are obtained, and compared with the predicted values by the unmixing-mixing scheme. The numerical results show the usefulness of the proposed model to predict the thermoelastic behavior of porous composites.

• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.35-39
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• 2005

선형혼합분광분석(LSU, Linear Spectral Unmixing) 모델은 위성 영상의 한 화소 값이 공간 내에 포함된 다양한 지표 대상물의 반사에너지가 혼합된 결과로 나타난다는 가정을 통해 화소이하(Sub-Pixel) 단위의 영상 분석을 수행하는 알고리즘의 한 형태이다. 분석의 결과는 한 화소에 존재하는 순수 대상물(Endmember)의 비율로 나타나며, 최소제곱법을 이용하여 결과를 도출하는 것이 일반적인 방법으로 알려져 있다. 하지만, 최소제곱법을 이용한 선형혼합분광분석모델은 기본적인 가정을 만족시키지 못하며, Endmember를 사용자가 임의로 지정해야 하기 때문에 영상 분석에 많은 어려움이 있다. 이런 단점을 극복하기 위해 무감독으로 추출된 Endmember를 이용한 제약선형분광혼합분석(Constrained Linear Spectral Unmixing) 모델을 본 연구를 통해 제안하고자 한다. 결과를 통해, 무감독 제약선형분광혼합분석 모델은 선형분광혼합분석 모델에 비해 각각의 Endmember에 대하여 제약조건을 만족하는 점유비율(Abundance) 정보를 제공하였으나, 비슷한 Endmember를 중복 추출할 수 있는 가능성도 지니고 있음을 확인할 수 있었다.