• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.395-402
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• 2018

Human palms contain discriminative features for proving the identity of each person. In this paper, we present a novel method for user verification based on palmprints and palm veins. Specifically, the region of interest (ROI) is first determined to be forced to include the maximum amount of information with respect to underlying structures of a given palm image. The extracted ROI is subsequently enhanced by directional patterns and statistical characteristics of intensities. For multispectral palm images, each of convolutional neural networks (CNNs) is independently trained. In a spirit of ensemble, we finally combine network outputs to compute the probability of a given ROI image for determining the identity. Based on various experiments, we confirm that the proposed ensemble method is effective for user verification with palmprints and palm veins.

• Proceedings of the KSRS Conference
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• v.1
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• pp.184-187
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• 2006

The radiometric correction is prerequisite to derive both land and ocean surface properties from optical remote sensing data. Radiometric calibration of remotely sensed data has traditionally been accomplished by means of vicarious ground calibration techniques. The purpose of this study is to calibrate the radiometric characteristic of Airborne Multispectral Scanner (AMS) by field campaign. In order to calibrate the AMS data, four different spectral tarps which are 3.5%, 23%, 35%, and 53% were validated by GER-3700 that is the surface reflectance measurement equipment and were utilized. After validation of the spectral tarps, each reflectance from the spectral tarps was compared with Digital Number (DN) value of AMS. There was very high correlation between tarp reflectance and DN value of AMS so that radiometric calibration of AMS data has been accomplished by those results. The calibrated AMS data were validated with in-situ measured reflectance data from artificial and natural target. Also QuickBird image data were used for verifying the results of AMS radiometric calibration. This presentation discusses the results of the above tests.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.46-46
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• 2017

We developed a rice simulation model for remote-sensing (SIMRIW-RS, Homma et al., 2007) to evaluate rice production and management on a regional scale. Here, we reports its application trial to estimate rice production in farmers' fields in Sendai, Japan. The remote-sensing data for the application was periodically obtained by multispectral camera (RGB + NIR and RedEdge) attached with unmanned air vehicle (UAV). The airborne images was 8 cm in resolution which was attained by the flight at an altitude of 115 m. The remote-sensing data was relatively corresponded with leaf area index (LAI) of rice and its spatial and temporal variation, although the correspondences had some errors due to locational inaccuracy. Calibration of the simulation model depended on the first two remote-sensing data (obtained around one month after transplanting and panicle initiation) well predicted rice growth evaluated by the third remote-sensing data. The parameters obtained through the calibration may reflect soil fertility, and will be utilized for nutritional management. Although estimation accuracy has still needed to be improved, the rice yield was also well estimated. These results recommended further data accumulation and more accurate locational identification to improve the estimation accuracy.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.57-60
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• 2008

The flow of freshwater into the sea, termed as submarine groundwater discharge, is a key factor for understanding the hydrological cycle in both the sea and land regions. The numerous positions from which freshwater gushes out or its quantity impedes the understanding of its properties. Therefore, this study detects groundwater discharge points arising due to the difference in freshwater and seawater by using the multispectral Landsat ETM+ signals. A case study in coastal regions around Mt. Chokaisan, Japan is performed. This study comprises three procedures: (1) computer simulation of the flow of submarine groundwater discharge in the study area, (2) performance of preliminary experiment on the band properties of the Landsat ETM+, (3) detection of the difference in water properties by using the Landsat multispectral bands. Our experimental results obtained by the Landsat ETM+ are in considerable agreement with the realities in the study area.

• Journal of Astronomy and Space Sciences
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• v.13 no.2
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• pp.198-209
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• 1996

In this paper, we present an image compression algorithm that is capable of significantly reducing the vast mount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet trans-form to remove the spatial redundancy. The transformed images are than encoded by hilbert-curve scanning and run-length-encoding, followed by huffman coding. We also present the performance of the proposed algorithm with KITSAT-1 image as well as the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by peak signal to noise ratio (PSNR) and classification capability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.120-127
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• 2000

In this paper, we propose multispectral image compression using classified interband prediction and vector quantization in wavelet domain. This method classifies each region considering reflection characteristics of each band in image data. In wavelet domain, we perform the classified intraband VQ to remove intraband redundancy for a reference band image that has the lowest spatial variance and the best correlation with other band. And in wavelet domain, we perform the classifled interband prediction to remove interband redundancy for the remaining bands. Then error wavelet coefficients between original image and predicted image are intraband vector quantized to reduce prediction error. Experiments on remotely sensed satellite image show that coding efficiency of theproposed method is better than that of the conventional method.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.359-367
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• 2011

A suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. The research on pan-sharpening algorithm in improving the accuracy of image classification has been reported. For a classification, preserving the spectral information is important. Other applications such as road detection depend on a sharp and detailed display of the scene. Various criteria applied to scenes with different characteristics should be used to compare the pan-sharpening methods. The pan-sharpening methods in our research comprise rather common techniques like Brovey, IHS(Intensity Hue Saturation) transform, and PCA(Principal Component Analysis), and more complex approaches, including wavelet transformation. The extraction of matching pairs was performed through SIFT descriptor and Canny edge detector. The experiments showed that pan-sharpening techniques for spatial enhancement were effective for extracting point and linear features. As a result of the validation it clearly emphasized that a suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. In future it is necessary to design hybrid pan-sharpening for the updating of features and land-use class of a map.

• ETRI Journal
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• v.33 no.4
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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.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1165-1178
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• 2018

Collapse of an Antarctic ice shelf and its flow velocity changes has the potential to reduce the restraining stress to the seaward flow of the Antarctic Ice Sheet, which can cause sea level rising. In this study, variations in ice velocity from 2000 to 2017 for the Nansen Ice Shelf in East Antarctica that experienced a large-scale collapse in April 2016 were analyzed using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images. To extract ice velocity, image matching based on orientation correlation was applied to the image pairs of blue, green, red, near-infrared, panchromatic, and the first principal component image of the Landsat multispectral data, from which the results were combined. The Landsat multispectral image matching produced reliable ice velocities for at least 14% wider area on the Nansen Ice Shelf than for the case of using single band (i.e., panchromatic) image matching. The ice velocities derived from the Landsat multispectral image matching have the error of $2.1m\;a^{-1}$ compared to the in situ Global Positioning System (GPS) observation data. The region adjacent to the Drygalski Ice Tongue showed the fastest increase in ice velocity between 2000 and 2017. The ice velocity along the central flow line of the Nansen Ice Shelf was stable before 2010 (${\sim}228m\;a^{-1}$). In 2011-2012, when a rift began to develop near the ice front, the ice flow was accelerated (${\sim}255m\;a^{-1}$) but the velocity was only about 11% faster than 2010. Since 2014, the massive rift had been fully developed, and the ice velocity of the upper region of the rift slightly decreased (${\sim}225m\;a^{-1}$) and stabilized. This means that the development of the rift and the resulting collapse of the ice front had little effect on the ice velocity of the Nansen Ice Shelf.