• Ocean and Polar Research
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• 제37권1호
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• pp.11-22
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• 2015

In-situ measurements are labor-intensive, time-consuming, and limited in their ability to observe currents with spatial variations in the surf zone. This paper proposes an optical image-based method of measurement of currents in the surf zone. This method measures nearshore currents by tracking in time wave breaking-induced foam patches from sequential images. Foam patches in images tend to be arrayed with irregular pixel intensity values, which are likely to remain consistent for a short period of time. This irregular intensity feature of a foam patch is characterized and represented as a keypoint using an image-based object recognition method, i.e., Scale Invariant Feature Transform (SIFT). The keypoints identified by the SIFT method are traced from time sequential images to produce instantaneous velocity fields. In order to remove erroneous velocities, the instantaneous velocity fields are filtered by binding them within upper and lower limits, and averaging the velocity data in time and space with a certain interval. The measurements that are obtained by this method are comparable to the results estimated by an existing image-based method of observing currents, named the Optical Current Meter (OCM).

• 대한원격탐사학회지
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• 제27권5호
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• pp.565-572
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• 2011

본 연구에서는 초분광영상의 분광혼합분석을 위한 endmember를 효율적으로 추출할 수 있는 알고리즘을 개발하였다. 본 기법은 N-FINDR기법의 장점과 IEA기법의 장점을 혼합한 형태로서, 추출하고자하는 endmemebr의 개수 등 사전 입력변수를 전혀 요구하지 않는다. 또한 반복계산 과정에서 단계별로spectral unmixing을 수행하므로 endmember별 abundance fraction을 최종 결과물로 생성한다. USGS의 분광라이브러리를 이용하여 생성한 모의 초분광 영상자료에의 시험적용 결과, endmember의 개수와 반사특성, abundance fraction이 매우 정확하게 추출되고 있음을 확인할 수 있었다. 향후, 영상 영역 내에 단일 물질로 순수하게 100% 피복된 pure pixel이 존재하지 않는 경우가 흔히 발생하는 실제 초분광 영상자료에의 적용성 시험을 위한 연구가 진행될 예정이다.

• 대한원격탐사학회지
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• 제29권4호
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• pp.399-406
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• 2013

위성 영상 자료에 대한 품질의 향상과 안정화는 다양한 목적을 가진 사용자들을 만족시킬 수 있다. 특히 절대 방사 검/보정은 영상의 광항적 품질을 유지하기 위한 척도가 된다. 본 연구에서는 초분광 영상 밴드 접합 기법과 분광 반응도를 이용하여 다중 분광 센서의 가상화를 통해 절대 방사 보정 계수의 적합성을 판단하였다. 적합성 분석을 위해 약 30분 차이로 촬영된 EO-1 Hyperion과 Landsat-8 OLI 센서의 영상을 이용하였고, 서로 다른 특성을 지닌 토지 피복으로 구성된 3개 지역을 선정하여 복사 에너지 값을 비교 하였다. 그 결과, 시공간에 따른 차이, 센서 수준의 차이를 제외하고 모든 밴드에서 0.99 이상의 적합성을 보여 주었다.

• 농업과학연구
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• 제42권3호
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• pp.269-276
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• 2015

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we discussed the state-of-the-art of the domestic and international use of UAV in agricultural sector as well as assessed its utilization and applicability for agricultural environment in Korea. Association of robotic, computer vision and geomatic technologies have established a new paradigm of low-altitude aerial remote sensing that has now been receiving attention from researchers all over the world. In a field study, it has been found that use of UAV imagery in an agricultural subsidy program can reduce the farmers' complain and provide objective evidence. UAV high resolution photography can also be helpful in monitoring the disposal zone for animal carcasses. Due to its expeditiousness and accuracy, UAV imagery can be a very useful tool to evaluate the damage in case of an agricultural disaster for both parties insurance companies and the farmers. Also high spatial and temporal resolution in UAV system can increase the prediction accuracy which in turn help to maintain the agricultural supply and demand chain.

• 대한원격탐사학회지
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• 제28권6호
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• pp.683-691
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• 2012

High resolution satellite images are now widely used for a variety of mapping applications including photogrammetry, GIS data acquisition and visualization. As the spectral and spatial data size of satellite images increases, a greater processing power is needed to process the images. The solution of these problems is parallel systems. Parallel processing techniques have been developed for improving the performance of image processing along with the development of the computational power. However, conventional CPU-based parallel computing is often not good enough for the demand for computational speed to process the images. The GPU is a good candidate to achieve this goal. Recently GPUs are used in the field of highly complex processing including many loop operations such as mathematical transforms, ray tracing. In this study we proposed a technique for parallel processing of high resolution satellite images using GPU. We implemented a spectral radiometric processing algorithm on Landsat-7 ETM+ imagery using CUDA, a parallel computing architecture developed by NVIDIA for GPU. Also performance of the algorithm on GPU and CPU is compared.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1329-1331
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• 2003

Improvement of the classification accuracy is one of the major concerns in the field of remote sensing application research in recent years. Previous research shows that the accuracy of the conventional classification methods based only on the original spectral information were usually unsatisfied and need to be refined by manual edit. This present paper describes a method of combining the image processing, ancillary data (such as digital elevation model) and expert knowledge (especially the knowledge of local professionals) to improve the efficiency and accuracy of the satellite image classification in hilly land. Firstly, the Landsat TM data were geo-referenced. Secondly, the individual bands of the image were intensitynormalized and the normalized difference vegetation index (NDVI) image was also generated. Thirdly, a set of sample pixels (collected from field survey) were utilized to discover their corresponding DN (digital number) ranges in the NDVI image, and to explore the relationships between land use type and its corresponding spectral features . Then, using the knowledge discovered from previous steps as well as knowledge from local professionals, with the support of GIS technology and the ancillary data, a set of conditional statements were applied to perform the TM imagery classification. The results showed that the integration of image processing and spatial analysis functions in GIS improved the overall classification result if compared with the conventional methods.

• 한국측량학회지
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• 제33권3호
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• pp.211-218
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• 2015

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

• 대한원격탐사학회지
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• 제25권3호
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• pp.271-285
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• 2009

Multi-temporal satellite imagery can be changed into a transform image that emphasizes the changed area only through the application of various change detection techniques. From the transform image, an automated change detection model calculates the optimal threshold value for classifying the changed and unchanged areas. However, the model can cause undesirable results when the histogram of the transform image is unbalanced. This is because the model uses a single threshold value in which the sign is either positive or negative and its value is constant (e.g. -1, 1), regardless of the imbalance between changed pixels. This paper proposes an advanced method that can improve accuracy by applying separate threshold values according to the increased or decreased range of the changed pixels. It applies multiple threshold values based on the cumulative producer's and user's accuracies in the automated binary change detection model, and the analyst can automatically extract more accurate optimal threshold values. Multi-temporal IKONOS satellite imagery for the Daejeon area was used to test the proposed method. A total of 16 transformation results were applied to the two study sites, and optimal threshold values were determined using accuracy assessment curves. The experiment showed that the accuracy of most transform images is improved by applying multiple threshold values. The proposed method is expected to be used in various study fields, such as detection of illegal urban building, detection of the damaged area in a disaster, etc.

• 대한원격탐사학회지
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• 제20권3호
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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.

• 대한원격탐사학회지
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• 제24권1호
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• pp.57-64
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• 2008

In the previous study, the Haar wavelet was used as the sole basis function for the 3D discrete wavelet transform because the number of bands is too small to decompose a remotely sensed image in band direction with other basis functions. However, it is possible to use other basis functions for wavelet decomposition in horizontal and vertical directions because wavelet decomposition is independently performed in each direction. This study aims to classify a high spatial resolution image with the six types of basis function including the Haar function and to compare those results. The other wavelets are more helpful to classify high resolution imagery than the Haar wavelet. In overall accuracy, the Coif4 wavelet has the best result. The improvement of classification accuracy is different depending on the type of class and the type of wavelet. Using the basis functions with long length could be effective for improving accuracy in classification, especially for the classes of small area. This study is expected to be used as fundamental information for selecting optimal basis function according to the data properties in the 3D DWT based image classification.