• 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.

• Journal of Biosystems Engineering
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• v.40 no.4
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• pp.417-427
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• 2015

Purpose: This study investigated different band selection methods to classify spectrally similar data - obtained from aerial images of healthy citrus canopies and citrus greening disease (Huanglongbing or HLB) infected canopies - using small differences without unmixing endmember components and therefore without the need for an endmember library. However, large number of hyperspectral bands has high redundancy which had to be reduced through band selection. The objective, therefore, was to first select the best set of bands and then detect citrus Huanglongbing infected canopies using these bands in aerial hyperspectral images. Methods: The forward feature selection algorithm (FFSA) was chosen for band selection. The selected bands were used for identifying HLB infected pixels using various classifiers such as K nearest neighbor (KNN), support vector machine (SVM), naïve Bayesian classifier (NBC), and generalized local discriminant bases (LDB). All bands were also utilized to compare results. Results: It was determined that a few well-chosen bands yielded much better results than when all bands were chosen, and brought the classification results on par with standard hyperspectral classification techniques such as spectral angle mapper (SAM) and mixture tuned matched filtering (MTMF). Median detection accuracies ranged from 66-80%, which showed great potential toward rapid detection of the disease. Conclusions: Among the methods investigated, a support vector machine classifier combined with the forward feature selection algorithm yielded the best results.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.165-169
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• 2006

Purpose of this research is estimation of water depth by hyperspectral remote sensing in area that access of ship is difficult This research used EO-1 Hyperion satellite imagery. Atmospheric and geometric correction is executed. Compress of band used MNF transforms. Diffuse Attenuation Coefficient of target area is decided in imagery for water depth estimation. Determination of Emdmember in pixel is using Linear Spectral Unmixing techniques. Water depth estimated using this result.

• 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.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.509-516
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• 2013

Since traditional linear feature extraction methods are unable to handle nonlinear characteristics often exhibited in hyperspectral imagery, nonlinear feature extraction, also known as manifold learning, is receiving increased attention in hyperspectral remote sensing society as well as other community. A most widely used manifold Isomap is generally promising good results in classification and spectral unmixing tasks, but significantly high computational overhead is problematic, especially for large scale remotely sensed data. A small subset of distinguishing points, referred to as landmarks, is proposed as a solution. This study proposes a new robust and controllable landmark selection method based on the maximum volume of the simplex spanned by landmarks. The experiments are conducted to compare classification accuracies with standard deviation according to sampling methods, the number of landmarks, and processing time. The proposed method could employ both classification accuracy and computational efficiency.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.716-723
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• 2008

With rapid development of science and technology and recent widening of mankind's range of activities, development of coastal waters and the environment have emerged as global issues. In relation to this, to allow more extensive analyses, the use of satellite images has been on the increase. This study aims at utilizing hyperspectral satellite images in determining the depth of coastal waters more efficiently. For this purpose, a partial image of the research subject was first extracted from an EO-1 Hyperion satellite image, and atmospheric and geometric corrections were made. Minimum noise fraction (MNF) transformation was then performed to compress the bands, and the band most suitable for analyzing the characteristics of the water body was selected. Within the chosen band, the diffuse attenuation coefficient Kd was determined. By deciding the end-member of pixels with pure spectral properties and conducting mapping based on the linear spectral unmixing method, the depth of water at the coastal area in question was ultimately determined. The research findings showed the calculated depth of water differed by an average of 1.2 m from that given on the digital sea map; the errors grew larger when the water to be measured was deeper. If accuracy in atmospheric correction, end-member determination, and Kd calculation is enhanced in the future, it will likely be possible to determine water depths more economically and efficiently.