• 대한원격탐사학회지
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• 제24권3호
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• pp.257-266
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• 2008

This study presents an approach to synthesize multispectral images at a higher resolution by exploiting a high-resolution image acquired in panchromatic modality. The synthesized images should be similar to the multispectral images that would have been observed by the corresponding sensor at the same high resolution. The proposed scheme is designed to reconstruct the multispectral images at the higher resolution with as less color distortion as possible. It uses a regression model of the second order to fit panchromatic data to multispectral observations. Based on the regression model, the multispectral images at the higher spatial resolution of the panchromatic image are optimized by a quadratic programming. In this study, the new method was applied to the IKONOS 1m panchromatic and 4m multispectral data, and the results were compared with them of several current approaches. Experimental results demonstrate that the proposed scheme can achieve significant improvement over other methods.

• 대한원격탐사학회지
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• 제24권5호
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• pp.525-533
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• 2008

This study presents a pansharpening method, so called FitPAN, to synthesize multispectral images at a higher resolution by exploiting a high-resolution image acquired in panchromatic modality. FitPAN is a modified version of the quadratic programming approach proposed in (Lee, 2008), which is designed to generate synthesized multispectral images similar to the multispectral images that would have been observed by the corresponding sensor at the same high resolution. The proposed scheme aims at reconstructing the multispectral images at the higher resolution with as less spectral distortion as possible. This study also proposes a sharpening process to eliminate some distortions appeared in the fused image of the higher resolution. It employs the Point-Jacobian MAP iteration utilizing the contextual information of the original panchromatic image. In this study, the new method was applied to the IKONOS 1m panchromatic and 4m multispectral data, and the results were compared with them of several current approaches. Experimental results demonstrate that the proposed scheme can achieve significant improvement in both spectral and block distortion.

• 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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.18-21
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• 2008

Lee (2008) proposed the pansharpening method to reconstruct at the higher resolution the multispectral images which agree with the spectral values observed from the sensor of the lower resolution values. It outperformed over several current techniques for the statistical analysis with quantitative measures, and generated the imagery of good quality for visual interpretation. However, if a small object stretches over two adjacent pixels with different spectral characteristics at the lower resolution, the pixels of the object at the higher resolution may have different multispectral values according to their location even though they have a same intensity in the panchromatic image of higher resolution. To correct this problem, this study employed an iterative technique similar to the image restoration scheme of Point-Jacobian iterative MAP estimation. The effect of pansharpening on image segmentation/classification was assessed for various techniques. The method was applied to the IKONOS image acquired over the area around Anyang City of Korea.

• 한국측량학회지
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• 제35권1호
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• pp.1-10
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• 2017

An UAV (Unmanned Aerial Vehicle) is a flight system that is designed to conduct missions without a pilot. Compared to traditional airborne-based photogrammetry, UAV-based photogrammetry is inexpensive and can obtain high-spatial resolution data quickly. In this study, we aimed to classify the land cover using high-spatial resolution images obtained using a UAV. An RGB camera was used to obtain high-spatial resolution orthoimage. For accurate classification, multispectral image about same areas were obtained using a multispectral sensor. A DSM (Digital Surface Model) and a modified NDVI (Normalized Difference Vegetation Index) were generated using images obtained using the RGB camera and multispectral sensor. Pixel-based classification was performed for twelve classes by using the RF (Random Forest) method. The classification accuracy was evaluated based on the error matrix, and it was confirmed that the proposed method effectively classified the area compared to supervised classification using only the RGB image.

• ETRI Journal
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• 제33권4호
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• pp.497-505
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• 2011

Image fusion is a technical method to integrate the spatial details of the high-resolution panchromatic (HRP) image and the spectral information of low-resolution multispectral (LRM) images to produce high-resolution multispectral images. The most important point in image fusion is enhancing the spatial details of the HRP image and simultaneously maintaining the spectral information of the LRM images. This implies that the physical characteristics of a satellite sensor should be considered in the fusion process. Also, to fuse massive satellite images, the fusion method should have low computation costs. In this paper, we propose a fast and efficient satellite image fusion method. The proposed method uses the spectral response functions of a satellite sensor; thus, it rationally reflects the physical characteristics of the satellite sensor to the fused image. As a result, the proposed method provides high-quality fused images in terms of spectral and spatial evaluations. The experimental results of IKONOS images indicate that the proposed method outperforms the intensity-hue-saturation and wavelet-based methods.

• Journal of Biosystems Engineering
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• 제43권2호
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• pp.138-147
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• 2018

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.

• 대한원격탐사학회지
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• 제33권6_1호
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• pp.1003-1017
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• 2017

지표면의 공간 정보를 정확히 추출하기 위해서는 고 해상도의 다중 분광 영상 자료를 사용할 필요가 있다. 범색 영상에 비해 상대적으로 낮은 공간 해상도를 갖는 다중 분광 자료의 해상도를 범색 영상 급으로 높이기 위해 PAN-sharpening 융합 기술을 사용한다. 이러한 고해상도 자료를 분석하기 위해서는 화소기반보다는 객체 기반 분석이 주목을 받고 있다. 객체 기반 영상 분석을 위해서 영상을 구성하는 화소들의 집단으로 영상 객체를 생성하는 영상 분할 과정이 선행되어야 한다. RAG(Regional Adjancy Graph)에 의해 형성된 인접 지역을 합병하는 지역 확장을 통해 효과적으로 영상 분할을 할 수 있다. 위성 원격 탐사에서 불 완전한 관측 환경으로 수집한 영상 자료에 질 저하가 일어 난다. 정확한 영상 분할을 위해서 동일 지역으로 관측된 분광 값의 변이가 최소화되도록 질의 개선이 필요하다. 동일 지역에 속하는 공간적으로 인접한 이웃들의 화소 값과 차이를 반복적으로 줄여 나가는 과정을 통해 동일 지역에서의 화소 값의 변이를 감소시킬 수 있다. 영상 객체를 단위로 사용하는 영상 분류에서 오류를 감소시키기 위해 영상 분할 결과에서 적정한 분할 지역 크기를 생성하여야 한다. 분할 지역 크기는 지역 확장 과정에서 합병을 중지하는 단계에 의해 정해지므로 중지 규칙은 영상 분할 결과의 품질을 결정한다. 본 연구에서는 모의 자료 실험을 통하여 분할의 정확성에 대해 정량적 평가를 실시하였으며 3개의 PAN-sharpened 고해상도 다중 분광 영상 자료에 대해 적용하여 복원의 효과에 대해 실험하였다. 실제 자료의 분석에서는 중지 규칙과 관련된 분할 지역 크기에 대해 정성적으로 평가 하였다. 사용된 원격 탐사 자료는 1m급의 미국 LA지역에서 수집된 Dubaisat-2 자료와 0.7 m급의 한반도 대전 지역과 충청남도 지역에서 각각 수집된 KOMPSAT-3 자료이다. 실험 결과는 영상 복원은 PAN-sharpened 고해상도 다중 분광 자료의 영상 분할 결과의 정확성을 상당히 제고시킬 수 있다는 것을 보여준다.

• 한국측량학회지
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• 제36권3호
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• pp.135-152
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• 2018

The purpose of this study is to propose an optimal fusion method of aerial multi - sensor data to improve the accuracy of land cover classification. Recently, in the fields of environmental impact assessment and land monitoring, high-resolution image data has been acquired for many regions for quantitative land management using aerial multi-sensor, but most of them are used only for the purpose of the project. Hyperspectral sensor data, which is mainly used for land cover classification, has the advantage of high classification accuracy, but it is difficult to classify the accurate land cover state because only the visible and near infrared wavelengths are acquired and of low spatial resolution. Therefore, there is a need for research that can improve the accuracy of land cover classification by fusing hyperspectral sensor data with multispectral sensor and aerial laser sensor data. As a fusion method of aerial multisensor, we proposed a pixel ratio adjustment method, a band accumulation method, and a spectral graph adjustment method. Fusion parameters such as fusion rate, band accumulation, spectral graph expansion ratio were selected according to the fusion method, and the fusion data generation and degree of land cover classification accuracy were calculated by applying incremental changes to the fusion variables. Optimal fusion variables for hyperspectral data, multispectral data and aerial laser data were derived by considering the correlation between land cover classification accuracy and fusion variables.

• 한국측량학회지
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• 제39권1호
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• pp.13-21
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• 2021

아리랑3호, 국토위성 등 고해상도 국토관측 위성은 일반적으로 가시광 및 근적외선 영역의 영상을 획득하기 위한 MS (Multispectral) CCD (Charge Coupled Device) 센서와 MS보다 4배의 공간해상도를 갖는 고해상도 PAN (Panchromatic) 영상을 획득하기 위한 CCD 센서의 조합으로 된 카메라를 탑재한다. 카메라 내에서 PAN과 MS CCD라인이 일정한 간격을 갖게 설치되기 때문에 위성이 궤도를 지나가며 대상물을 약간의 시간차를 갖고 촬영하게 되며 따라서 영상 내의 대상물 위치도 달라진다. PAN과 MS 영상융합을 위해서는 PAN과 MS영상간의 정밀한 상호좌표등록이 필요한데, 본 연구에서는 센서모델링을 통한 기법과 영상 매칭의 융합을 통한 상호좌표등록을 수행하였다. PAN과 MS 상호 센서모델링을 통해 초기 상호좌표등록을 수행하고, 영상 매칭을 통해 그 정밀도를 향상시켜 약 RMSE (Root Mean Square Error) 0.2 화소의 정밀도를 확보할 수 있었다.