• Korean Journal of Remote Sensing
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• v.24 no.5
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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.

• Journal of Sensor Science and Technology
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• v.15 no.5
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• pp.341-346
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• 2006

We have fabricated SOI CMOS active pixel image sensor with the pinned photodiode on handle wafer in order to reduce dark currents and improve spectral response. The structure of the active pixel image sensor is 4 transistors APS which consists of a reset and source follower transistor on seed wafer, and is comprised of the photodiode, transfer gate, and floating diffusion on handle wafer. The source of dark current caused by the interface traps located on the surface of a photodiode is able to be eliminated, as we apply the pinned photodiode. The source of dark currents between shallow trench isolation and the depletion region of a photodiode can be also eliminated by the planner process of the hybrid bulk/SOI structure. The photodiode could be optimized for better spectral response because the process of a photodiode on handle wafer is independent of that of transistors on seed wafer. The dark current was about 6 pA at 3.3 V of floating diffusion voltage in the case of transfer gate TX = 0 V and TX=3.3 V, respectively. The spectral response of the pinned photodiode was observed flat in the wavelength range from green to red.

• Proceedings of the KSRS Conference
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• 2008.03a
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• pp.195-200
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• 2008

The accuracy of classifying pixels in HIRIS images is usually degraded by noisy bands since noisy bands may deform the typical shape of spectral reflectance. Proposed in this paper is a statistical method for noisy band removal which mainly makes use of the correlation coefficients between bands. Considering each band as a random variable, the correlation coefficient measures the strength and direction of a linear relationship between two random variables. While the correlation between two signal bands is high, existence of a noisy band will produce a low correlation due to ill-correlativeness and undirectedness. The application of the correlation coefficient as a measure for detecting noisy bands is under a two-pass screening scheme. This method is independent of the prior knowledge of the sensor or the cause resulted in the noise. The classification in this experiment uses the unsupervised k-nearest neighbor algorithm in accordance with the well-accepted Euclidean distance measure and the spectral angle mapper measure. This paper also proposes a hierarchical combination of these measures for spectral matching. Finally, a separability assessment based on the between-class and within-class scatter matrices is followed to evaluate the performance.

• Journal of Biosystems Engineering
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• v.36 no.3
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• pp.180-186
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• 2011

The objective of this research was to establish the estimation method of growth information on potato using Multi-Spectral Image Sensor (MSIS) and Global Positioning System (GPS). And growth estimation map for determining a prescription map over the entire field was generated. To determine the growth model, 10 ground-truth points of areas of $4m^2$ each were selected and investigated. The growth information included stem number, crop height and SPAD value. In addition, images information involving the ground-truth points were also taken by an unmanned helicopter, and reflectance value of Green, Red, and NIR bands were calculated with image processing. Then, growth status of potato was modeled by multi-regression analysis using these reflectance value of Green, Red, and NIR. As a result, potato growth information could be detected by analyzing Green, Red, and NIR images. Stem number, crop height and SPAD value could be estimated with $R^2$ values of 0.600, 0.657 and 0.747 respectively. The generated GIS map would describe variability of the potato growth in a whole field.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.449-457
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• 2011

As automated image processing techniques have been required in multi-temporal/multi-sensor geospatial image applications, use of automated but highly invariant image matching technique has been a critical ingredient. Note that there is high possibility of geometric and spectral differences between multi-temporal/multi-sensor geospatial images due to differences in sensor, acquisition geometry, season, and weather, etc. Among many image matching techniques, the SIFT (Scale Invariant Feature Transform) is a popular method since it has been recognized to be very robust to diverse imaging conditions. Therefore, the SIFT has high potential for the geospatial image processing. This paper presents a performance test results of the SIFT on geospatial imagery by simulating various image differences such as shear, scale, rotation, intensity, noise, and spectral differences. Since a geospatial image application often requires a number of good matching points over the images, the number of matching points was analyzed with its matching positional accuracy. The test results show that the SIFT is highly invariant but could not overcome significant image differences. In addition, it guarantees no outlier-free matching such that it is highly recommended to use outlier removal techniques such as RANSAC (RANdom SAmple Consensus).

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.3 s.309
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• pp.25-30
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• 2006

An infrared imaging method is proposed in which direct measurement of the spectral response of CCD sensors can be achieved through digital image processing. This method allows for a simple and economic method to detect the spectral sensitivity of commercialized CCD sensors. The key components of the apparatus are a monochromator, CCD-sample supporter and a personal computer equipped with a digital image processing systems. Tentative experimentation conducted on the commercialized CCD camera has resulted in a fairly consistent agreement with the theoretical model.

• Korean Journal of Geomatics
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• v.5 no.1
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• pp.27-34
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• 2005

Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

• Journal of Forest and Environmental Science
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• v.29 no.2
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• pp.131-137
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• 2013

This study attempts to classify forest species using hyperspectral data for supporting resources management. The primary dataset used was AISA sensor. The sensor was mounted onboard the NOMAD GAF-27 aircraft at 2,000 m altitude creating a 2 m spatial resolution on the ground. Pre-processing was carried out with CALIGEO software, which automatically corrects for both geometric and radiometric distortions of the raw image data. The radiance data set was then converted to at-sensor reflectance derived from the FODIS sensor. Spectral Angle Mapper (SAM) technique was used for image classification. The spectra libraries for tree species were established after confirming the appropriate match between field spectra and pixel spectra. Results showed that the highest spectral signature in NIR range were Kembang Semangkok (Scaphium macropodum), followed by Meranti Sarang Punai (Shorea parvifolia) and Chengal (Neobalanocarpus hemii). Meanwhile, the lowest spectral response were Kasai (Pometia pinnata), Kelat (Eugenia spp.) and Merawan (Hopea beccariana), respectively. The overall accuracy obtained was 79%. Although the accuracy of SAM techniques is below the expectation level, SAM classifier was able to classify tropical tree species. In future it is believe that the most effective way of ground data collection is to use the ground object that has the strongest response to sensor for more significant tree signatures.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.1
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• pp.37-46
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• 2004

In this paper, we propose an improved reproduction algorithm for a realistic image of the real scene based on the spectral distribution of lights and objects. The proposed method for the realistic image is focused on a more accurate reproduction of an image incident on the sight of the viewer. At first, to reproduce an image accurately incident on a sight of viewer, we used the backward ray tracing method based on spectral distribution of object and illuminant representing its physical characteristic used in real. Next, we propose utilizing the improved shading model of the reproduction algorithm of realistic image by applying Bouguer-Beer's law to consider an optical absorptive property of transparent objects. We also define a new ambient light term which is considered the diffuse reflection of neighboring objects instead of constant ambient light. The simulation results show that the proposed algorithm can reproduce the visually similar image with a scene incident on a sight of viewer.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.123-131
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• 2016

To capture color and near-infrared images simultaneously, a multi-spectral filter array(MSFA) sensor is used. This is because an NIR band gives additional invisible information to human eyes to see subject under extremely low light level. However, because lenses have different refractive indices for different wavelengths, lenses may fail to focus widely different rays to the same convergence point. This is why a chromatic aberration(CA) problem occurs and images are degraded. In this paper, the image restoration algorithm for an MSFA image, which removes the CA problem, is presented. The obtained MSFA image is filtered by the estimated low-pass kernel to generate a base image. This base image is used to remove CA problem in multi-spectral(MS) images. By modeling the image degradation process and by using the least squares approach of the difference between the high-frequencies of the base and MS images, the desired high-resolution MS images are reconstructed. The experimental results show that the proposed algorithm performs well in estimating the high-quality MS images and reducing the chromatic aberration problem.