• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.1-7
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• 2016

In this paper, we proposed real-time relative radiometric calibration processing method for SWIR(Short Wavelength Infra-Red) sensor using 'Hyper-Spectral Imager'. Until now domestic research for Hyper-Spectral Imager has been performing with foreign sensor device. So we have been studying hyper spectral sensor device to meet domestic requirement, especially military purpose. To improve detection & identify capability in 'Hyper-Spectral Imager', it is necessary to expend sensing wavelength from visual and NIR(Near Infra-Red) to SWIR. We aimed to design real-time processor for SWIR sensor which can control the sensor ROIC(Read-Out IC) and process calibrate the image. To build Hyper-Spectral sensor device, we will review the SWIR sensor and its signal processing board. And we will analyze relative radiometric calibration processing method and result. We will explain several SWIR sensors, our target sensor and its control method, steps for acquisition of reference images and processing result.

• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.27-34
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• 2017

In this paper, we propose a novel radiometric calibration method which can effectively compensate the nonlinearity of the detector for hyper-spectral camera. In general, the detector of hyper-spectral camera can produce nonlinear output depending on radiance and integral time. The conventional radiometric calibration methods extract the imprecise radiance profile from the spectral profile of the target due to this nonlinearity. In our proposed method, we use a quadratic equation instead of a linear equation to describe the relation between output of detector and radiance. Then, we use a fractional function to compensate variation of integration time. Thus, our proposed method can extract more precise spectral profile of radiance than conventional radiometric calibration method.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1055-1057
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• 2003

In this research, the concrete with paint film was classified using hyper-spectral remote sensing. First, spectral characteristics of concrete and concrete with some kinds of paint films were investigated with a spectrometer. Second, using reflectance and first order derivative, spectral characteristics of the normal concrete and the concrete with paint film were classified. By using hyper-spectral remote sensing, not only extraction of crack but also inspection of paint film distribution is possible.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.111-116
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• 2021

The hyper-spectral image is data obtained by dividing the electromagnetic wave band in the infrared region into hundreds of wavelengths. It is used to find or classify objects in various fields. Recently, deep learning classification method has been attracting attention. In order to use hyper-spectral image data as deep learning training data, a processing technique is required compared to conventional visible light image data. To solve this problem, we developed a software that selects specific wavelength images from the hyper-spectral data cube and performs the ground truth task. We also developed software to manage data including environmental information. This paper describes the configuration and function of the software.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.820-824
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• 2003

One of the most important research areas on remote sensing is spectral unmixing of hyper-spectral data. For spectral unmixing of hyper spectral data, accurate land cover information is necessary. But obtaining accurate land cover information is difficult process. Obtaining land cover information from high-resolution data may be a useful solution. In this study spectral signature of endmembers on ASTER acquired in October was calculated from land cover information on IKONOS acquired in September. Then the spectral signature of endmembers applied to ASTER images acquired on January and March. Then the result of spectral unmxing of them evauateted. The spectral signatures of endmembers could be applied to different seasonal images. When it applied to an ASTER image which have similar zenith angle to the image of the spectral signatures of endmembers, spectral unmixing result was reliable. Although test data has different zenith angle from the image of spectral signatures of endmembers, the spectral unmixing results of urban and vegetation were reliable.

• Proceedings of the KIEE Conference
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• 2007.11a
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• pp.140-141
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• 2007

본 연구에서는 고분해능 및 고감도화된 시스템 개발을 위하여 AOTF를 이용하여 하이퍼스펙트럼 영상기법을 활용한 전임상 영상장비에 대한 연구를 수행하였다. 제작된 고감도 하이퍼스펙트럼 분자영상 시스템의 생물학적 적용을 위하여, AOTF의 파장 또는 진동수를 변화시키면서 GFP가 발현된 HEK 293 세포의 이미지를 촬영하였다. 또한, 제작된 실험 대상물 이미지화 시스템을 이용해서 실험용 쥐의 이미지를 촬영하였다. 실험용 쥐를 크세논 아크램프 사용 전 후 이미지를 촬영한 결과 크세논 아크 램프 사용 후에는 청색의 선명한 영상을 얻을 수 있었다.

• Proceedings of the Optical Society of Korea Conference
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• 2009.02a
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• pp.217-218
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• 2009

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.107-112
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• 2022

In an embedded system such as a drone, it is difficult to store, transfer and analyze the entire hyper-spectral image to a server in real time because it takes a lot of power and time. Therefore, the hyper-spectral image data is transmitted to the server through dimension reduction or compression pre-processing. Feature selection method are used to send only the bands for analysis purpose, and these algorithms usually take a lot of processing time depending on the size of the image, even though the efficiency is high. In this paper, by improving the temporal disadvantage of the band selection algorithm, the time taken 24 hours was reduced to around 60-180 seconds based on the 40000*682 image resolution of 8GB data, and the use of 7.6GB RAM was significantly reduced to 2.3GB using 45 out of 150 bands. However, in terms of pixel classification performance, more than 98% of analysis results were derived similarly to the previous one.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.233-242
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• 2009

This paper presents two different types of supervised classifiers such as support vector machine (SVM) and spectral angle mapper (SAM). The Compact Airborne Spectrographic Imager (CASI) high resolution aerial image was classified with the above two classifier. The image was classified into eight land use /land cover classes. Accuracy assessment and Kappa statistics were estimated for SVM and SAM separately. The overall classification accuracy and Kappa statistics value of the SAM were 69.0% and 0.62 respectively, which were higher than those of SVM (62.5%, 0.54).

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.65-65
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• 2008

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