• 대한원격탐사학회지
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• 제3권1호
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• pp.11-23
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• 1987

본 연구는 LANDSAT MSS 및 TM 영상자료를 이용하여 낙동강 하천수가 유입, 확산되는 과정을 시간적, 공간적으로 추적조사하여, 이들이 해양환경에 미치는 영향을 구명하는데 중점을 두었다. 본 연구로부터 현탁물질량의 농도는 고분도와 같이 계절, 대.소조기, 창.낙조류, 강우량에 따라 큰 변동을 가져오며, 연안전선의 형성 및 분포에 대한 시간적, 공간적인 변동상태를 광역적으로 추적할 수 있었고 해안선, 퇴적형, 천해역의 해저지형 변동조사는 기존의 해도 및 육도를 최신화할 수 있는 정보로써 위성자료의 가치가 크다는 결론을 얻었다.

• 대한원격탐사학회지
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• 제23권5호
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• pp.455-464
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• 2007

Lee(2007) suggested the Point-Jacobian iteration MAP estimation(PJIMAP) for noise removal of the images that are corrupted by multiplicative speckle noise. It is to find a MAP estimation of noisy-free imagery based on a Bayesian model using the lognormal distribution for image intensity and an MRF for image texture. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. In this study, the MAP estimation is computed by the Point-Jacobian iteration using adaptive parameters. At each iteration, the parameters related to the Bayesian model are adaptively estimated using the updated information. The results of the proposed scheme were compared to them of PJIMAP with SAR simulation data generated by the Monte Carlo method. The experiments demonstrated an improvement in relaxing speckle noise and estimating noise-free intensity by using the adaptive parameters for the Ponit-Jacobian iteration.

• 대한원격탐사학회지
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• 제24권5호
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• pp.473-481
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• 2008

This paper presents an approach to Back-propagation and Radial Basis Function neural network method with various training set for automatic vehicle detection from aerial images. The initial extraction of candidate object is based on Mean-shift algorithm with symmetric property of a vehicle structure. By fusing the density and the symmetry, the method can remove the ambiguous objects and reduce the cost of processing in the next stage. To extract features from the detected object, we describe the object as a log-polar shape histogram using edge strengths of object and represent the orientation and distance from its center. The spatial histogram is used for calculating the momentum of object and compensating the direction of object. BPNN and RBFNN are applied to verify the object as a vehicle using a variety of non-car training sets. The proposed algorithm shows the results which are according to the training data. By comparing the training sets, advantages and disadvantages of them have been discussed.

• 대한원격탐사학회지
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• 제19권5호
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• pp.381-392
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• 2003

Multi-temporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land-cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. A harmonic model that can represent seasonal variability is characterized by four components: mean level, frequency, phase and amplitude. The trigonometric components of the harmonic function inherently contain temporal information about changes in land-cover characteristics. Using the estimates which are obtained from sequential images through spectral analysis, seasonal periodicity can be incorporates into multi-temporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 ～ 2000 using a dynamic technique. Land-cover types were then classified both with the estimated harmonic components using an unsupervised classification approach based on a hierarchical clustering algorithm. The results of the classification using the harmonic components show that the new approach is potentially very effective for identifying land-cover types by the analysis of its multi-temporal behavior.

• 대한원격탐사학회지
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• 제16권3호
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• pp.235-242
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• 2000

본 논문에서는 그림자 정보를 사용하여 위성 영상에서 건물을 검출하는 기법을 제안한다. 비교적 일정한 밝기값 분포를 가지는 건물을 검출하기위해 영상을 건물, 그림자 그리고 배경의 세가지 영역으로 분류한다. 건물 영역 및 그림자 영역에 대해 잡음을 제거하고 그림자 영역에 인접한 건물을 건물과 그림자 크기에 대한 제약 조건을 적용하여 검출한다. 본 논문에 사용된 영상은 KOMPSAT 위성영상과 SPOT 위성영상을 사용하였으며 위성영상내의 건물을 효과적으로 검출할 수 있었다.

• 대한원격탐사학회지
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• 제30권4호
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• pp.465-480
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• 2014

본 연구에서는 최근 발생한 집중호우 사례들 중 예보가 어려워 피해가 컸던 두 사례(2010년 9월 21일, 2011년 8월 9일)에 대해 적외영상과 수증기영상의 시 공간적인 변화 특성을 분석하였다. 두 사례에서 한반도지역에 집중호우를 유발한 대류 세포들은 적외영상에서 하층운이 광범위하게 분포하고 수증기 영상에서는 명역과 암역의 경계(boundary)에서 생성되는 특징을 보였다. 또한 대류 세포들의 이동속도 차에 의한 총 5번의 병합과정 중 4번의 병합과정에서 대류 세포들의 병합 후 대류 세포는 더욱 발달되었으며 강수 강도도 급격하게 강화되었다. 대류시스템에서의 강우강도 변화는 휘도온도의 평균보다 최소 휘도온도의 시간적 변화와 밀접하게 관련된 것으로 판단되며 대류 세포들의 병합도 집중호우의 강도 변화에 영향을 주는 주요 인자로 생각된다. 대류 세포들의 병합은 영상동화를 통해 어느 정도 예측이 가능하지만 대류 세포의 탐지는 적외 및 수증기 영상 모두에서 일정 강도 이상 발달한 상태에서만 탐지가 가능하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.126-129
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• 2006

The marine environment is under considerable threat from intentional or accidental oil spills, ballast water discharged, dredging and infilling for coastal development, and uncontrolled sewage and industrial wastewater discharges. Monitoring spills and illegal oil discharges is an important component in ensuring compliance with marine protection legislation and general protection of the coastal environments. For the monitoring task an image processing system is needed that can efficiently perform the detection and the tracking of oil spills and in this direction a significant amount of research work has taken place mainly with the use of radar (SAR) remote sensing data. In this paper the level set image segmentation technique was tested for the detection of oil spills. Level set allow the evolving curve to change topology (break and merge) and therefore boundaries of particularly intricate shapes can be extracted. Experimental results demonstrated that the level set segmentation can be used for the efficient detection and monitoring of oil spills, since the method coped with abrupt shape’s deformations and splits.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.650-650
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• 2004

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1474-1474
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• 2003