• 한국물환경학회지
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• 제30권3호
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• pp.329-339
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• 2014

This study aims to estimate chlorophyll-a concentration in rivers using multi-spectral RapidEye imagery and Spectral Mixture Analysis (SMA) and assess the applicability of SMA for multi-temporal imagery analysis. Comparison between images (acquired on Oct. and Nov., 2013) predicted and ground reference chlorophyll-a concentration showed significant performance statistically with determination coefficients of 0.49 and 0.51, respectively. Two band (Red-RE) model for the October and November 2013 RapidEye images showed low performance with coefficient of determinations ($R^2$) of 0.26 and 0.16, respectively. Also Three band (Red-RE-NIR) model showed different performance with $R^2$ of 0.016 and 0.304, respectively. SMA derived Chlorophyll-a concentrations showed relatively higher accuracy than band ratio models based values. SMA was the most appropriate method to calculate Chlorophyll-a concentration using images which were acquired on period of low Chlorophyll-a concentrations. The results of SMA for multi-temporal imagery showed low performance because of the spatio-temporal variation of each end members. This approach provides the potential of providing a cost effective method of monitoring river water quality and management using multi-spectral imagery. In addition, the calculated Chlorophyll-a concentrations using multi-spectral RapidEye imagery can be applied to water quality modeling, enhancing the predicting accuracy.

• 한국지리정보학회지
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• 제15권3호
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• pp.52-65
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• 2012

작물과 산림을 포함한 식생에 대한 순1차 생산(net primary production, NPP)와 총1차 생산(gross primary production, GPP)은 바이오매스와 식생의 탄소저장과 밀접한 관련이 있으며, 원격탐사를 이용해 바이오매스를 추정하는 많은 노력이 이루어지고 있다. 바이오매스는 광합성에 매우 중요한 요소인 클로로필(엽록소)의 총 함유량으로 추정할 수 있는데, 클로로필을 추정하기 위해서 다양한 식생지수들이 개발되었다. 식생지수들은 개발에 사용된 식생의 종류와 원격탐사 데이터에 따라 조금씩 차이를 가지고 있다. 하이퍼스펙트럴 영상은 다중분광 영상에 비하여 세분화된 각 파장대마다 물질에 따른 반사 및 흡수 특성이 다르기 때문에, 기존의 식생지수를 그대로 사용하기에 무리가 따른다. 본 연구는 항공기 탑재 하이퍼스펙트럴 영상을 이용하여 산림에 대한 바이오매스 추정을 위한 매개변수로 활용되는 적합한 식생지수는 무엇인지 평가하는 것을 목적으로 한다. 이를 위해 하이퍼스펙트럴 영상의 밴드 특성을 고려하여 다수의 식생지수 산출식 중 9개를 선정하고, SMA(spectral mixture analysis)를 통하여 대상지역의 산림을 대표하는 3개의 endmember를 추출하였다. 9개의 식생지수와 추출된 endmembers의 상관관계를 분석하였다. 상관분석 결과는 산림이 분포된 지역에서 Pearson 상관계수는 MTVI1과 TVI가 0.877의 상관계수를 가졌으며, 식생이 적고 토양의 분포가 확연한 지역에서는 MCARI가 0.9061로 매우 높은 상관계수를 보였다. 전반적으로 MTVI1과 TVI이 0.757의 동일한 상관계수를 가지며 식생에 대한 3개의 endmember를 가장 잘 설명하는 것으로 나타났다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 심포지엄 논문집 정보 및 제어부문
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• pp.99-100
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• 2008

This paper proposes a Gaussian Mixture Model (GMM) - based speech emotion recognition methods using four feature parameters; 1) Fast Fourier Transform(FFT) spectral entropy, 2) delta FFT spectral entropy, 3) Mel-frequency Filter Bank (MFB) spectral entropy, and 4) delta MFB spectral entropy. In addition, we use four emotions in a speech database including anger, sadness, happiness, and neutrality. We perform speech emotion recognition experiments using each pre-defined emotion and gender. The experimental results show that the proposed emotion recognition using FFT spectral-based entropy and MFB spectral-based entropy performs better than existing emotion recognition based on GMM using energy, Zero Crossing Rate (ZCR), Linear Prediction Coefficient (LPC), and pitch parameters. In experimental Results, we attained a maximum recognition rate of 75.1% when we used MFB spectral entropy and delta MFB spectral entropy.

• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2004년도 GIS/RS 공동 춘계학술대회 논문집
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• pp.493-498
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• 2004

The objective of the present study is to compare various conventional and recently evolved satellite image-processing techniques and to ascertain the best possible technique that can identify and position of aquaculture farms accurately in and around the Younggwang coastal area. Several conventional techniques performed to extract such information fiom the Landsat-TM imagery do not seem to yield better information about the aquaculture farms, and lead to misclassification. The large errors between the actual and extracted aquaculture farm information are due to existence of spectral confusion and inadequate spatial resolution of the sensor. This leads to possible occurrence of mixture pixels or 'mixels' of the source of errors in the classification techniques. Understanding the confusing and mixture pixel problems requires the development of efficient methods that can enable more reliable extraction of aquaculture farm information. Thus, the more recently evolved methods such as the step-by-step partial spectral end-member extraction and linear spectral unmixing methods are introduced. The farmer one assumes that an end-member, which is often referred to as 'spectrally pure signature' of a target feature, does not appear to be a spectrally pure form, but always mix with the other features at certain proportions. The assumption of the linear spectral unmxing is that the measured reflectance of a pixel is the linear sum of the reflectance of the mixture components that make up that pixel. The classification accuracy of the step-by-step partial end-member extraction improved significantly compared to that obtained from the traditional supervised classifiers. However, this method did not distinguish the aquaculture ponds and non-aquaculture ponds within the region of the aquaculture farming areas. In contrast, the linear spectral unmixing model produced a set of fraction images for the aquaculture, water and soil. Of these, the aquaculture fraction yields good estimates about the proportion of the aquaculture farm in each pixel. The acquired proportion was compared with the values of NDVI and both are positively correlated (R$^2$ =0.91), indicating the reliability of the sub-pixel classification.ixel classification.

• 대한원격탐사학회지
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• 제20권6호
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• pp.369-382
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• 2004

본 연구는 Terra ASTER 영상과 LANDSAT 7 ETM+ 분광 영상정보와 같은 상이한 방사 및 공간 해상도를 갖는 위성 센서의 영상을 지질학적으로 활용하기 위한 선형분광혼합분석(LSMA: Linear Spectral Mixture Analysis)기법의 적용성을 목적으로 한다 실제 적용사례로서 몽골지역을 대상으로 ASTER 영상과 LANDSAT 7 ETM+ 분광 영상정보를 이용하여 지질학적 주제도 자성과정을 수행하였다. 두 영상 정보에 대하여 기하 보정 및 방사 휘도 조정 등의 전처리 작업을 수행한 후 사전 지질조사 정보와 두 영상정보의 밴드 별 상관도를 분석하여 7개의 지질단위의 분광 클래스를 선택하였고 20개 밴드완 위성 영상자료를 LSMA 기법에 적용하였다. 처리 결과로 주제도 작성의 대상으로 한 7개의 지질단위에 대한 각각의 주제도를 얻게 되었다. 결론적으로 LSMA 기법은 지질 주제도 작성을 위한 효과적인 접근 방법 중의 하나로 판단된다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.297-300
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• 2006

The knowledge of fuel load and composition is important for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential of reduction the uncertainty in mapping fuels and offers the best approach for improving our abilities. This paper compared the results of object-oriented classification to a pixel-based classification for fuel type map derived from Hyperion hyperspectral data that could be enable to provide this information and allow a differentiation of material due to their typical spectra. Our methodological approach for fuel type map is characterized by the result of the spectral mixture analysis (SMA) that can used to model the spectral variability in multi- or hyperspectral images and to relate the results to the physical abundance of surface constitutes represented by the spectral endmembers. Object-oriented approach was based on segment based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery

• 대한원격탐사학회지
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• 제31권4호
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• pp.303-319
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• 2015

The objectives of this paper are to measure surface imperviousness using three different classification methods: per-pixel, sub-pixel, and object-oriented classification. They are tested on high-spatial resolution QuickBird data at 2.4 meters (four spectral bands and three principal component bands) as well as a medium-spatial resolution Landsat TM image at 30 meters. To measure impervious surfaces, we selected 30 sample sites with different land uses and residential densities across image representing the city of Phoenix, Arizona, USA. For per-pixel an unsupervised classification is first conducted to provide prior knowledge on the possible candidate spectral classes, and then a supervised classification is performed using the maximum-likelihood rule. For sub-pixel classification, a Linear Spectral Mixture Analysis (LSMA) is used to disentangle land cover information from mixed pixels. For object-oriented classification several different sets of scale parameters and expert decision rules are implemented, including a nearest neighbor classifier. The results from these three methods show that the object-oriented approach (accuracy of 91%) provides more accurate results than those achieved by per-pixel algorithm (accuracy of 67% and 83% using Landsat TM and QuickBird, respectively). It is also clear that sub-pixel algorithm gives more accurate results (accuracy of 87%) in case of intensive and dense urban areas using medium-resolution imagery.

• 대한원격탐사학회지
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• 제21권6호
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• pp.457-468
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• 2005

도시화에 따른 불투수 지표면의 증가는 도시환경에 부정적인 영향을 미치게 된다. 따라서 도시 내 불투수 지역의 시공간적 변화 사항을 탐색하고 정량화하는 작업은 도시환경을 연구함에 있어 무엇보다 중요한 일이라 할 수 있다 지난 시기 도시지역의 불투수 지표면을 탐색하는 방법으로는 전통적인 영상분류 기법이 많이 사용되었다. 그러나 기존의 전통적인 영상분류 기법은 영상을 구성하는 각 셀이 지표면에 존재하는 다양한 객체들의 분광특성이 혼합된 결과임에도 불구하고 단 하나의 클래스로만 구분하는 단점을 가진다. 또한 불투수 지표면의 비율을 산정하기 위해서는 영상분류 후 각 분류항목에 불투수율을 할당해야하는 2중의 노력이 필요하며, 각 클래스에 단일한 불투수율을 지정해야만 하는 단점을 갖는다. 본 논문에서는 기존의 영상 분류방법이 갖는 이러한 단점을 보완하고자 불투수 지표면의 비율을 산정하기 위해 분광혼합분석 (spectral mixture analysis) 기법을 이용하였다. 분광혼합분석 기법을 적용하기 위해 식생, 토양, low albedo, high albedo 등 4가지 요소를 엔드멤버로 선택하였으며, 불투수율은 low albedo와 high albedo의 합으로 산정하였다. 대상 연구지역은 지난 십여년 동안 급격한 도시화가 진행된 탄천유역을 선정하였으며, 1988, 1994, 2001년의 Landsat 영상을 이용하여 신도시 건설에 따른 불투수 지표면의 변화율을 검토하였다. 분석결과 탄천유역의 불투수율은 88년 $15.6\%$, 94년 $20.1\%$, 2001년 $24\%$로 증가된 것으로 나타났다. 결론적으로 도시 불투수율을 분석 시 분광혼합분석 기법을 적용할 경우 추가적인 노력 없이 비교적 정확한 불투수율을 얻을 수 있음을 확인할 수 있었다.

• 대한약학회:학술대회논문집
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• 대한약학회 2000년도 NEW STRATEGY FOR DRUG DEVELOPMENT IN POST-GENOMIC ERA(대한약학회)
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• pp.251.1-251.1
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• 2000

• 대한원격탐사학회지
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• 제23권1호
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• pp.21-32
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• 2007

Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential to reduce the uncertainty in mapping fuels and offers the best approach for improving our abilities. Especially, Hyperspectral sensor have a great potential for mapping vegetation properties because of their high spectral resolution. The objective of this paper is to evaluate the potential of mapping fuel properties using Hyperion hyperspectral remote sensing data acquired in April, 2002. Fuel properties are divided into four broad categories: 1) fuel moisture, 2) fuel green live biomass, 3) fuel condition and 4) fuel types. Fuel moisture and fuel green biomass were assessed using canopy moisture, derived from the expression of liquid water in the reflectance spectrum of plants. Fuel condition was assessed using endmember fractions from spectral mixture analysis (SMA). Fuel types were classified by fuel models based on the results of SMA. Although Hyperion imagery included a lot of sensor noise and poor performance in liquid water band, the overall results showed that Hyperion imagery have good potential for wildfire fuel mapping.