• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.727-731
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• 2003

ASTER image has some advantages for classification such as 15 spectral bands and 15m ${\sim}$ 90m spatial resolution. However, in the classification using general remote sensing image, shadow areas are often classified into water area. It is very difficult to divide shadow and water. Because reflectance characteristics of water is similar to characteristics of shadow. Many land cover items are consisted in one pixel which is 15m spatial resolution. Nowadays, very high resolution satellite image (IKONOS, Quick Bird) and Digital Surface Model (DSM) by air borne laser scanner can also be used. In this study, mixed pixel analysis of ASTER image has carried out using IKONOS image and DSM. For mixed pixel analysis, high accurated geometric correction was required. Image matching method was applied for generating GCP datasets. IKONOS image was rectified by affine transform. After that, one pixel in ASTER image should be compared with corresponded 15×15 pixel in IKONOS image. Then, training dataset were generated for mixed pixel analysis using visual interpretation of IKONOS image. Finally, classification will be carried out based on Linear Mixture Model. Shadow extraction might be succeeded by the classification. The extracted shadow area was validated using shadow image which generated from 1m${\sim}$2m spatial resolution DSM. The result showed 17.2% error was occurred in mixed pixel. It might be limitation of ASTER image for shadow extraction because of 8bit quantization data.

• 한국측량학회지
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• 제16권1호
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• pp.75-84
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• 1998

인공위성 데이터로부터 보다 자세한 정보를 취득하기 위해 혼합화소를 해석하는 방법에 대해 연구하였다. 본 연구에서는 2가지 토지피복만 혼합되어 있는 경우로 한정하였다. 정준상관분류기법에서 생성되는 정준벡터를 분석하여 혼합화소를 분류해 낼 수 있었으며, 그 기준으로 정준벡터의 요소인 정준가중치 2개의 상대적 비율을 역치로 사용하였다. 9월 1일을 전후한 TM 데이터의 다리와 물 항목에 대한 분류의 경우에, 혼합화소를 가장 적절히 분류하는 역치는 4.0으로 결정되었다. 즉 정준가중치사이의 비율이 4.0이상이면 단일피복화소이며, 4.0이하이면 혼합화소로 간주하게 된다. 정준가중치의 분포에 의해 대략적인 토지피복 구성비율도 추정할 수 있다. 실험영역에 대한 혼합화소 추출의 정확도는 90%로서 높은 수준이었다. 따라서 정준벡터분석에 의한 혼합화소 분류방법은 효용성이 있다고 판단된다.

• 대한공간정보학회지
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• 제3권2호
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• pp.91-100
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• 1995

본 논문은 위성탐사 이미지의 분류에서 한 화소(SPOT HRV의 밴드 1-3의 경우 $200{\times}20m$)에 포함된 혼합된 정보의 분류를 시도한다. 먼저 기존의 분류기법에서 발생되는 정보의 손실과 혼합화소에 내포된 정보의 불확실성에 대해 알아보고 이를 해결하기 위한 방법으로 피지 시그모이드 함수와 역전파 신경망을 이용한 기법을 제안하며, 이를 실험하고 비교 분석한다.

• 한국산업융합학회 논문집
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• 제7권1호
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• pp.41-46
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• 2004

This paper proposes a separation of mixed signals by using the robust independent component analysis(RICA) based on neural networks. RICA is based on the temporal correlations and the second order statistics of signal. This method e is applied for improving the analysis rate and speed in which the sources have very small or zero kurtosis. The proposed method has been applied for separating the 10 mixed finger prints of $256{\times}256$-pixel and the 4 mixed images of $512{\times}512$-pixel, respectively. The simulation results show that RICA has the separating rate and speed better than those using the conventional FP algorithm based on Newton method.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.359-362
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• 2004

In multi-dimensional image, ICA-based feature extraction algorithm, which is proposed in this paper, is for the purpose of detecting target feature about pixel assumed as a linear mixed spectrum sphere, which is consisted of each different type of material object (target feature and background feature) in spectrum sphere of reflectance of each pixel. Landsat ETM+ satellite image is consisted of multi-dimensional data structure and, there is target feature, which is purposed to extract and various background image is mixed. In this paper, in order to eliminate background features (tidal flat, seawater and etc) around target feature (laver farm) effectively, pixel spectrum sphere of target feature is projected onto the orthogonal spectrum sphere of background feature. The rest amount of spectrum sphere of target feature in the pixel can be presumed to remove spectrum sphere of background feature. In order to make sure the excellence of feature extraction method based on ICA, which is proposed in this paper, laver farm feature extraction from Landsat ETM+ satellite image is applied. Also, In the side of feature extraction accuracy and the noise level, which is still remaining not to remove after feature extraction, we have conducted a comparing test with traditionally most popular method, maximum-likelihood. As a consequence, the proposed method from this paper can effectively eliminate background features around mixed spectrum sphere to extract target feature. So, we found that it had excellent detection efficiency.

• 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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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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.

• 전자공학회논문지 IE
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• 제46권4호
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• pp.8-15
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• 2009

본 논문은 CMOS 이미지센서 SPICE 회로 해석을 위한 포토다이오드 및 픽셀 모델링을 나타내었다. 소자 시뮬레이터인 메디치(Medici)를 이용하여 입사광의 세기에 따른 광전류 특성을 확보하고 SPICE 시뮬레이션에서 활용하기 위한 SPICE용 포토 다이오드 모델을 개발하였다. 그리고 그 결과를 검증하기 위하여 포토다이오드와 NMOS로 구성된 시험용 회로구조에 대한 메디치(Medici)의 mixed mode 시뮬레이션 결과와 SPICE 시뮬레이션 결과를 비교하였다.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2006년도 춘계학술발표회 논문집
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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.

• 대한원격탐사학회지
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• 제34권2_2호
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• pp.339-349
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• 2018

황해 녹조 대발생은 2008년 이후 매년 여름 대규모로 발생하고 있으며, 인공위성 분석을 통해 녹조 규모를 추정하기 위한 연구가 활발하게 진행되고 있다. 하지만 정확한 녹조 면적 산출을 위한 위성영상 선택 기준이 마련되어 있지 않은 실정이다. 따라서 이 연구에서는 위성영상의 공간해상도에 따른 녹조 면적 산출 결과의 차이를 알아보고, 녹조 면적 산출에 적합한 위성영상을 제시하고자 한다. 이 연구에서 이용한 위성영상은 Landsat ETM+, MODIS, GOCI로 영상의 공간해상도는 각각 30, 250, 500 m 이다. NDVI 알고리즘을 적용하여 녹조 픽셀을 분류하였고, 임계값에 따른 녹조 면적 산출 결과를 비교하였다. 또한, Linear Spectral Unmixing(LSU) 기법을 이용하여 한 픽셀 내에 녹조가 차지하는 비율을 추정하였고, 녹조 비율의 차이가 면적 계산에 미치는 영향을 평가하였다. NDVI 알고리즘을 이용한 녹조 면적 산출 결과, 공간해상도가 낮을수록 녹조 면적이 과대추정되는 경향을 보였고, 최대 1.5배 차이를 보였다. 또한, LSU 분석 결과에서 픽셀 내의 녹조 비율이 0.1(10%) 미만인 픽셀이 대부분이었고, 0.5(50%) 이상 녹조 비중을 차지하는 픽셀은 세 타입의 영상 모두에서 약 2% 수준으로 극히 적었다. 즉, NDVI 분석 결과에서 녹조로 분류된 픽셀의 경우 한 픽셀의 공간을 녹조가 100% 채우고 있지 않더라도 모두 동일 면적으로 간주되기 때문에 실제보다 과대추정되는 것으로 생각되고, 이러한 현상은 공간해상도 차이에 의해 심화되는 것으로 나타났다.