• 대한원격탐사학회지
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• 제35권4호
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• pp.561-571
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• 2019

In this paper, we analyze the effect of the representative spectral indices, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) on classification accuracies of Landsat-8 OLI image.After creating these spectral index images, we propose five methods to select the spectral index images as classification features together with Landsat-8 OLI bands from 1 to 7. From the experiments we observed that when the spectral index image of NDVI or NDWI is used as one of the classification features together with the Landsat-8 OLI bands from 1 to 7, we can obtain higher overall accuracy and kappa coefficient than the method using only Landsat-8 OLI 7 bands. In contrast, the classification method, which selected only NDBI as classification feature together with Landsat-8 OLI 7 bands did not show the improvement in classification accuracies.

• 대한원격탐사학회지
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• 제36권4호
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• pp.627-635
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• 2020

With the increasing severity of climate change, intense torrential rains are occurring more frequently globally. Flooding due to torrential rain not only causes substantial damage directly, but also via secondary events such as landslides. Therefore, accurate and prompt flood detection is required. Because it is difficult to directly access flooded areas, previous studies have largely used satellite images. Traditionally, water indices such asthe normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) which are based on different optical bands acquired by satellites, are used to detect floods. In addition, as flooding likelihood is greatly influenced by the weather, synthetic aperture radar (SAR) images have also been used, because these are less influenced by weather conditions. In this study, we compared flood areas calculated from SAR images and water indices derived from Landsat-8 images, where the images were acquired at similar times. The flooded area was calculated from Landsat-8 and Sentinel-1 images taken between the end of May and August 2019 at Lijiazhou Island, China, which is located in the Changjiang (Yangtze) River basin and experiences annual floods. As a result, the flooded area calculated using the MNDWI was approximately 21% larger on average than that calculated using the NDWI. In a comparison of flood areas calculated using water indices and SAR intensity images, the flood areas calculated using SAR images tended to be smaller, regardless of the order in which the images were acquired. Because the images were acquired by the two satellites on different dates, we could not directly compare the accuracy of the water-index and SAR data. Nevertheless, this study demonstrates that floods can be detected using both optical and SAR satellite data.

• 시스템엔지니어링학술지
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• 제17권2호
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• 한국측량학회지
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• 제22권3호
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• pp.261-268
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• 2004

위성영상자료를 통해 각 파장대별로 기록된 지표면에 대한 반사특성정보로 정규산화율지수(NBR)나 정규식생지수(NDVI)를 구하여 산림에 대한 분석을 할 수 있다. 따라서 본 연구에서는 산불이 발생한 강릉시 사천면 지역을 중심으로 산불 발생이전 산림이 온전하였던 시기의 영상과 산불 발생 직 후, 발생 1년 후 그리고 2년 후의 영상으로 정규산화율지수와 정규식생지수를 각각 구하여 이 지수를 비교 분석함으로써 정규산화율지수의 효용성을 강조하였다. 그 결과 NBR영상간 차이가 NDVI영상간 차이보다 큰 범위의 동적변화를 보이고 있으므로 산불 피해강도나 식생회복상태 분석 시 NBR영상을 사용하는 것이 좋을 것으로 판단되며, 산불피해 지역에서도 NBR 영상이 NDVI영상보다 산림피해강도나 회복상태를 더욱 뚜렷하게 보여주고 있다.

• International Journal of Internet, Broadcasting and Communication
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• 제7권1호
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• pp.36-41
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• 2015

This paper is proposed a wavelet-based MCDT(Mask Coefficient Differential and Threshold) method of image registration of Multi-images contaminated with visible image and infrared image. The method for ensure reliability of the image registration is to the increase statistical corelation as getting the common feature points between two images. The method of threshold the wavelet coefficients using derivatives of the wavelet coefficients of the detail subbands was proposed to effectively registration images with distortion. And it can define that the edge map. Particularly, in order to increase statistical corelation the method of the normalized mutual information. as similarity measure common feature between two images was selected. The proposed method is totally verified by comparing with the several other multi-image and the proposed image registration.

• 대한원격탐사학회지
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• 제34권4호
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• pp.625-638
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• 2018

In this paper, we derive i) a function to estimate snow cover fraction (SCF) from a MODIS satellite image that has a wide observational area and short re-visit period and ii) a function to determine snow depth from the estimated SCF map. The SCF equation is important for estimating the snow depth from optical images. The proposed SCF equation is defined using the Gaussian function. We found that the Gaussian function was a better model than the linear equation for explaining the relationship between the normalized difference snow index (NDSI) and the normalized difference vegetation index (NDVI), and SCF. An accuracy test was performed using 38 MODIS images, and the achieved root mean square error (RMSE) was improved by approximately 7.7 % compared to that of the linear equation. After the SCF maps were created using the SCF equation from the MODIS images, a relation function between in-situ snow depth and MODIS-derived SCF was defined. The RMSE of the MODIS-derived snow depth was approximately 3.55 cm when compared to the in-situ data. This is a somewhat large error range in the Republic of Korea, which generally has less than 10 cm of snowfall. Therefore, in this study, we corrected the calculated snow depth using the relationship between the measured and calculated values for each single image unit. The corrected snow depth was finally recorded and had an RMSE of approximately 2.98 cm, which was an improvement. In future, the accuracy of the algorithm can be improved by considering more varied variables at the same time.

• 대한원격탐사학회지
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• 제38권6_4호
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• pp.1935-1943
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• 2022

위성 영상을 활용하여 대규모 또는 정밀 토양 수분도를 제작하는 방법의 개발과 이를 적용한 사례 연구는 원격탐사 응용 분야에서 중요한 연구 주제 중 하나이다. 이 연구는 제주도 연구 지역을 대상으로 토양 수분도를 제작하였다. 이를 위하여 선형으로 조정된 Synthetic Aperture Radar (SAR) 편광 영상과 입사각 정보를 이용하여 광학 영상과 함께 토양 수분도를 산출하였다. SAR 영상은 Google Earth Engine (GEE)에서 제공하는 후반 산란 계수 Analysis Ready Data (ARD) 자료를 사용하였다. 또한 Environmental Systems Research Institute (ESRI)의 토지 피복도(land cover map)와 KOMPSAT-3 고해상도 위성 영상의 지표 반사도로부터 산출한 식생 지수 정보(normalized difference vegetation index, NDVI)를 토양 수분도 처리 과정에 적용하였다. 이처럼 SAR 영상과 광학영상 정보를 융합하여 처리하는 경우는 토양 수분 산출물의 신뢰도를 향상할 수 있는 것으로 알려져 있다. 산출물의 과학적 분석을 위하여 KOMPSAT-3 영상으로 제작한 정규 수분 지수(normalized difference water index, NDWI)와 비교 분석을 실시하였다. 그리고 KOMPSAT-3 처리 결과의 검증을 위하여 Landsat-8 위성의 NDWI 처리 결과와 비교하였다. 이 연구를 통하여 산출한 토양 수분도 결과는 KOMPSAT-3 영상과 Landsat-8 위성으로 각각 처리한 NDWI 처리 결과와 높은 상관도를 나타냈다. 마지막으로 이 연구에 사용한 토양 수분 산출 알고리즘을 우리나라 고해상도 위성인 KOMPSAT-5 영상에 맞게 추가 개발하면 다른 외부 영상 없이 KOMPSAT 광학 위성정보와 KOMPSAT SAR 영상정보를 이용한 정밀 토양 수분도 제작이 가능할 것이라고 생각한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.385-388
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• 2003

In this paper, we propose a new saturation enhancement algorithm which is processed on the new color space, called Normalized YCbCr(NYCbCr). The algorithm consists of two processing unit. One is color space conversion from YCbCr to NYCbCr, and the other is using adaptive saturation mapping function(ASMF). NYCbCr color space is designed to prevent shortcomings such as luminance and hue shift of YCbCr color space and by saturation enhancement. ASMF is effective to enhance saturation properly for each image and to protect low saturation regions of color images from over-saturation. we verified our method using several color images. Experimental results show that the proposed method enhance the saturation with minimizing Luminance and Hue shift.

• 대한의용생체공학회:의공학회지
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• 제17권4호
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• pp.443-448
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• 1996

Sequential brain SPECT imaging has been used to assess the cerebral perfusion reserve(CPR) in cerebrovascular diseases(UD). We have realized parametric images of CPR using deadtime correction of gamma camera and normalized difference ratio. For the anatomical localization of CPR, the parametric images were registered to the contours of the cerebral regions using optimal threshold method, which showed to reflect the CPR more reliably and distinctively than the simple subtraction. We conclude that the quantitative estimation of CPR using normalized difference ratio image could be useflll for the diagnosis and prognostic assessment of CVD.

• 대한원격탐사학회지
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• 제39권6_1호
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• pp.1299-1308
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• 2023

기후변화 등으로 인해 전 세계적으로 산불이 점점 잦아지고 대형화되는 추세다. 위성영상 등의 원격탐사를 통한 산불피해 면적 및 피해강도를 산정하는 것은 현장조사에 따른 여러 가지 어려움을 줄일 수 있어 대안 및 보조자료로 활용이 가능하다. 산불피해강도(differenced normalized burn ratio, dNBR)는 산불 전후의 정규탄화지수(normalized burn ratio, NBR) 차이를 통해 산정하며, NBR 수식에 사용되는 영상은 Landsat의 근적외선(near infrared, NIR)과 단적외선(short-wavelength infrared, SWIR) 밴드를 기본으로 한다. 우리나라 위성영상의 경우, SWIR 밴드를 가지고 있지 않기 때문에 산불피해와 관련한 국내 연구들은 해외영상을 사용하거나 우리나라 위성영상을 사용한 경우, 정규식생지수(normalized difference vegetation index, NDVI)를 이용하여 간접적인 방법으로 dNBR을 산출하였다. 따라서 본 연구에서는 Kompsat-3A호(K3A)의 중적외선(mid-wavelength infrared, MWIR) 밴드를 NBR 수식의 SWIR 밴드 대신 대입하여 dNBR을 산정하고, dNBR의 기준이 되는 Landsat을 이용한 dNBR 결과 값과 비교하였다. 그 결과 K3A MWIR을 이용한 dNBR이 Landsat SWIR을 이용한 dNBR에 비해 나타낼 수 있는 값의 범위가 더 넓고 세분화하여 표현이 가능하였다. 따라서 산불피해 지역을 조사하는데 있어 K3A의 활용도가 높을 것이라 사료된다. 뿐만 아니라 본 연구에서는 30m로 열화된 K3A MWIR 밴드를 사용했으나 그보다 높은 해상도의 MWIR 밴드를 사용한다면 본 연구보다 훨씬 더 나은 결과를 얻을 수 있을 것이라 사료된다.