• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2008년도 International Meeting on Information Display
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• pp.1366-1369
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• 2008

We introduce new instrument that provides the spectral radiance at any incidence and azimuth angle in all the visible range. LCD emission and transmittance properties of display components can be measured precisely at each incidence and azimuth angle and wavelength. Full polarization spectral analysis can be also made.

• 한국측량학회지
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• 제31권6_2호
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• pp.559-565
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• 2013

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

• 한국측량학회지
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• 제35권1호
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• pp.11-22
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• 2017

In recent years, coastal dune erosion has accelerated as various structures have been developed around the coastal dunes. A land cover map should be developed to identify the characteristics of sand dunes and to monitor the condition of sand dunes. The Korean Ministry of Environment's land cover maps suffer from problems, such as limited classes, target areas, and durations. Thus, this study conducted experiments using RGB and multispectral images based on UAV (Unmanned Aerial Vehicle) over an approximately one-year cycle to create a land cover map of coastal dunes. RF (Random Forest) classifier was used for the analysis in accordance with the experimental region's characteristics. The pixel- and object-based classification results obtained by using RGB and multispectral cameras were evaluated, respectively. The study results showed that object-based classification using multispectral images had the highest accuracy. Our results suggest that constant monitoring of coastal dunes can be performed effectively.

• 한국측량학회지
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• 제33권6호
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• pp.547-555
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• 2015

The vegetation index is a crucial parameter in many biophysical studies of vegetation, and is also a valuable content in ecological processes researching. The OVIs (Optical Vegetation Index) that of using multispectral and hyperspectral data have been widely investigated in the literature, while the RVI (Radar Vegetation Index) that of considering volume scattering measurement has been paid relatively little attention. Also, there was only some efforts have been put to fuse the OVI with the RVI as an integrated vegetation index. To address this issue, this paper presents a novel FVI (Fusion Vegetation Index) that uses multispectral and full-PolSAR (Polarimetric Synthetic Aperture Radar) data. By fusing a NDVI (Normalized Difference Vegetation Index) of RapidEye and an RVI of C-band Radarsat-2, we demonstrated that the proposed FVI has higher separability in different vegetation types than only with OVI and RVI. Also, the experimental results show that the proposed index not only has information on the vegetation greenness of the NDVI, but also has information on the canopy structure of the RVI. Based on this preliminary result, since the vegetation monitoring is more detailed, it could be possible in various application fields; this synergistic FVI will be further developed in the future.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.755-764
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• 1996

A multispectral imaging system with selected wavelength optical filter was demonstrated feasible for food safety inspection. Intensified multispectral images of carcasses were obtained with visible/near-infrared optical filters(542-847 nm wavelengths) and analyzed. The analysis of textural features based on co-occurrence matrices was conducted to determine the feasibility of a multispectral image analyses for discriminating unwholesome poultry carcasses from wholesome carcasses. The mean angular second moment of the wholesome carcasses scanned at 542 nm wavelength was lower than that of septicemic (P$\leq$0.0005) and cadaver(P$\leq$0.0005) carcasses. On the other hand, for the carcasses scanned at 700nm wavelength , the feature values of septicemic and cadaver carcasses were significantly (P$\leq$0.0005) different from wholesome carcasses. The discriminant functions for classifying poultry carcasses into three classes (wholesome, septicemic , cadaver) were developed using linear and quadr tic covariance matrix analysis method. The accuracy of the quadratic discriminant models, expressed in rates of correct classification, were over 90% for the classification of wholesome, septicemic, and cadaver carcasses when textural features from the spectral images scanned at the wavelength of 542 and 700nm were utilized.

• 한국측량학회지
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• 제32권6호
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• pp.607-615
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• 2014

RVI (Radar Vegetation Index) has shown some promise in the vegetation fields, but its relationship with MVI (Multispectral Vegetation Index) is not known in the context of various land covers. Presented herein is a comparative analysis of the MVI values derived from the LANDSAT-8 and RVI values originating from the RADARSAT-2 quad-polarimetric SAR (Synthetic Aperture Radar) data. Among the various multispectral vegetation indices, NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were used for comparison with RVI. Four land covers (urban, forest, water, and paddy field) were compared, and the patterns were investigated. The experiment results demonstrated that the RVI patterns of the four land covers are very similar to those of NDVI and SAVI. Thus, during bad weather conditions and at night, the RVI data could serve as an alternative to the MVI data in various application fields.

• 경영과정보연구
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• 제3권
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• pp.15-45
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• 1999

Processing techniques of remote sensed image data using computer have been recognized very necessary techniques to all social fields, such as, environmental observation, land cultivation, resource investigation, military trend grasp and agricultural product estimation, etc. Especially, accurate classification and analysis to remote sensed image da are important elements that can determine reliability of remote sensed image data processing systems, and many researches have been processed to improve these accuracy of classification and analysis. Traditionally, remote sensed image data processing systems have been processed 2 or 3 selected bands in multiple bands, in this time, their selection criterions are statistical separability or wavelength properties. But, it have be bring up the necessity of bands selection method by data distribution characteristics than traditional bands selection by wavelength properties or statistical separability. Because data sensing environments change from multispectral environments to hyperspectral environments. In this paper for efficient data classification in multispectral bands environment, a band feature extraction method using the Rough sets theory is proposed. First, we make a look up table from training data, and analyze the properties of experimental multispectral image data, then select the efficient band using indiscernibility relation of Rough set theory from analysis results. Proposed method is applied to LANDSAT TM data on 2 June 1992. From this, we show clustering trends that similar to traditional band selection results by wavelength properties, from this, we verify that can use the proposed method that centered on data properties to select the efficient bands, though data sensing environment change to hyperspectral band environments.

• 한국측량학회지
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• 제33권3호
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• pp.155-161
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• 2015

Dike lines are important features for describing the detailed shapes of dikes and for detecting topographic changes on dike surfaces. Historically, dike lines have been generated using only the LiDAR data. This paper proposes a new methodology for selecting an appropriate dike line on various dike surfaces using the topographic LiDAR data and multispectral orthoimages taken in the Nakdong River basins. The fi rst baselines were generated from the given LiDAR data using the modified convex hull algorithm and smoothing spline function, and the second baselines were generated from the given orthoimages by the Canny operator. Next, one baseline was selected among the two baselines at 10m intervals by comparing their elevations, and the selected baseline at 10m interval was defined as the dike line segment. Finally, the selected dike line segments were connected to construct the 3D dike lines. The statistical results show that the dike lines generated using both the LiDAR data and multispectral orthoimages had the improved horizontal and vertical accuracies than the dike lines generated only using the LiDAR data on the various dike surfaces.

• 한국인터넷방송통신학회논문지
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• 제22권5호
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• pp.93-97
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• 2022

적외선 영상은 온도 정보뿐만 아니라 파장 정보를 포함하고 있으며 이는 단일 파장의 적외선 카메라로는 분석이 불가능하다. 다중 파장 적외선 카메라는 적외선 영상에 포함된 광범위한 정보를 획득할 수 있다. 다중 파장 적외선 카메라의 한 종류인 이중대역 적외선 카메라는 시스템 구성을 쉽게 할 수 있다는 이점이 있다. 이중대역 적외선 카메라는 적외선 영상에 포함된 온도정보 뿐만 아니라 파장 정보를 획득할 수 있고 이를 통해 적외선 카메라의 탐지/식별 성능을 향상시킬 수 있다. 본 논문에서는 중적외선 대역과 원적외선 대역을 동시에 획득할 수 있는 이중대역 적외선 검출기를 이용한 적외선 카메라의 설계에 대하여 기술한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 추계학술발표대회
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• pp.625-628
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• 2010

This paper proposes a novel feature extraction method for unsupervised multispectral image segmentation based in one dimensional combined neighborhood differences (1D CND). In contrast with the original CND, which is applied with traditional image, 1D CND is computed on a single pixel with various bands. The proposed algorithm utilizes the sign of differences between bands of the pixel. The difference values are thresholded to form a binary codeword. A binomial factor is assigned to these codeword to form another unique value. These values are then grouped to construct the 1D CND feature image where is used in the unsupervised image segmentation. Various experiments using two LANDSAT multispectral images have been performed to evaluate the segmentation and classification accuracy of the proposed method. The result shows that 1D CND feature outperforms the spectral feature, with average classification accuracy of 87.55% whereas that of spectral feature is 55.81%.