• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.527-535
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• 2013

Extraction of correct endmembers is prerequisite to successful spectral unmixing analysis. Various endmember extraction algorithms have been proposed and most experiments based on endmember extraction have used synthetic image and AVIRIS image data. However, these data can present different characteristics comparing with hyperspectral images acquired from real domestic environment. For this study, a test-bed was constructed for analysing the difference on diverse substances and sizes in domestic areas, and AISA hyperspectral imagery acquired from the test-bed was tested with two well-known endmember extraction algorithms: IEA, and N-FINDR. The results indicated that two different algorithms depended on the number of endmembers and material types in the test-bed. Therefore, optimized number of endmembers and characteristics of materials in test-bed site should be considered for the effective application of endmember extraction algorithms.

• 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.

• 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.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.347-355
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• 2012

A fast algorithm for endmember extraction is proposed in this study which extracts min. and max. pixels from each band after MNF transform as candidate pixels for endmember. This method finds endmembers not from the entire image pixels but only from the previously extracted candidate pixels. The experimental results by N-FINDR using a simulated hyperspectral image data and AVIRIS Cuprite image data showed that the proposed fast algorithm extracts the same endmembers with the conventional methods. More studies on the effect of noise and more adaptive criteria in extracting candidate pixels are expected to increase the usability of this method for more fast and efficient analysis of hyperspectral image data.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.219-226
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• 2014

Fractional values resulted from the spectral mixture analysis could be used to classify not only urban area with various materials but also forest area in more detailed spatial scale. Especially South Korea is largely consist of mixed forest, so the spectral mixture analysis is suitable as a classification method. For the successful classification using spectral mixture analysis, extraction of optimal endmembers is prerequisite process. Though geometric endmember selection has been widely used, it is barely suitable for forest area. Therefore, in this study, we modified Iterative Error Analysis (IEA), one of the most famous algorithms of image endmember selection which extracts pure pixel directly from the image. The endmembers which represent deciduous and coniferous trees are automatically extracted. The experiments were implemented on two sites of Compact Airborne Spectrographic Imager (CASI) and classified forest area into two types. Accuracies of each classification results were 86% and 90%, which mean proposed algorithm effectively extracted proper endmembers. For the more accurate classification, another substances like forest gap should be considered.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.197-205
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• 2004

Wetlands are lands with a mixture of water, herbaceous or woody vegetation and wet soil. And linear spectral mixture analysis (LSMA) is one of the most often used methods in handling the spectral mixture problem. This study aims to test LSMA is an enhanced routine for classification of wetland land-covers in Paldang reservoir and vicinity (paldang Reservoir) using Landsat TM and ETM+ imagery. In the LSMA process, reference endmembers were driven from scatter-plots of Landsat bands 3, 4 and 5, and a series of endmember models were developed based on green vegetation (GV), soil and water endmembers which are the main indicators of wetlands. To consider phenological characteristics of Paldang Reservoir, a soil endmember was subdivided into bright and dark soil endmembers in spring and a green vegetation (GV) endmember was subdivided into GV tree and GV herbaceous endmembers in fall. We found that LSMA fractions improved the classification accuracy of the wetland land-cover. Four endmember models provided better GV and soil discrimination and the root mean squared (RMS) errors were 0.011 and 0.0039, in spring and fall respectively. Phenologically, a fall image is more appropriate to classify wetland land-cover than spring's. The classification result using 4 endmember fractions of a fall image reached 85.2 and 74.2 percent of the producer's and user's accuracy respectively. This study shows that this routine will be an useful tool for identifying and monitoring the status of wetlands in Paldang Reservoir.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.565-572
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• 2011

A modified iterative N-FINDR algorithm is developed for fully automatic extraction of endmembers from hyperspectral image data. This algorithm exploits the advantages of iterative NFINDR technique and Iterative Error analysis technique. The experiments using a simulated hyperspectral image data shows that the optimum number of endmembers can be automatically decided. The extracted endmembers and finally generated abundance fraction maps show the potentialities of the proposed algorithm. More studies are needed for verification of the applicability of the algorithm to the real hyperspectral image data where the absence of pure pixels is common.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.411-419
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• 2003

An area corresponding to the spatial resolution of optical remote sensor imagery often includes more than one pure surface material. In such case, a pixel value represents a mixture of spectral reflectance of several materials within it. This study attempts to apply the spectral mixture analysis on forest and to evaluate the information content of endmember fractions resulted from the spectral unmixing. Landsat-7 ETM+ image obtained over the study area in the Kwangneung Experimental Forest was initially geo-referenced and radiometrically corrected to reduce the atmospheric and topographic attenuations. Linear mixture model was applied to separate each pixel by the fraction of six endmember: deciduous, coniferous, soil, built-up, shadow, and rice/grass. The fractional values of six endmember could be used to separate forest cover in more detailed spatial scale. In addition, the soil fraction can be further used to extract the information related to the canopy closure. We also found that the shadow effect is more distinctive at coniferous stands.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.52-65
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• 2012

The net biomass accumulation (or net primary production, NPP) and gross primary production (GPP) have closely related with carbon accumulations(or carbon exchange) in vegetation. There are many approaches to estimate biomass using remote sensing techniques. The vegetation indices (VIs) can be a methodology to estimate biomass which assumes total chlorophyll contents. Various VIs were characterized with difference development conditions as vegetation species, input datasets. The hyperspectral data have also different spatial/spectral resolutions for aerial surveying. Additionally they need particular spectral bands selection difficulty to calculate the VIs. The objective of this study is to evaluate the correlations with airborne hyperspectral data (compact airborne spectrographic imager, CASI) and spectral unmixing model (or spectral mixture analysis, SMA) to characterize vegetation indices in forest area. The spectral mixture analysis was used to model the spectral purity of each pixel as an endmember. The endmembers are the fraction components derived from hyperspectral data through the SMA. In this study, we choose three endmembers represented vegetation pixels in the hyperspectral data. These endmembers were compared with 9 VIs by the Pearson's correlation coefficient. The results show MTVI1 and TVI have same correlation coefficient with 0.877. The MCARI, especially has very high relationship with vegetation endmembers as 0.9061 at less vegetation and soil distributed site. The MTVI1 and TVI have high correlations with the vegetation endmembers as 0.757 in whole test sites.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.279-282
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• 2006

분광혼합은 위성영상에서 공간해상도의 한계로 인해 다른 분광 속성을 가진 물질들이 하나의 픽셀 내에 존재하게 될 때 발생하게 된다. 이러한 문제를 해결하고자 분광분리 알고리즘을 통해 픽셀의 순수한 영역만을 선정하여 정확도 높은 탐지가 가능하도록 하는 분광혼합분석(Spectral Mixture Analysis, 이하 SMA)을 고해상도 영상에 적용하였다. 본 연구는 산림의 훼손이 심각한 강원도 정선군 임계지역의 QuickBird 다중분광 위성영상을 이용하였다. 주성분분석(Principal Component Analysis, 이하 PCA)으로 생성된 결과 영상의 1, 2, 3번 밴드를 추출한 후에 밴드간의 Scatter plots 내에서 끝지점에 위치하는 Endmember를 3개(나지, 산림, 초지) 선정하였다. 선정된 Endmember를 토대로 작성된 fraction 영상을 이용하여 강원도 임계지역의 산림훼손으로 초지와 나지로 변화된 지역을 탐지하여 보았다.