• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.468-470
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• 2007

This paper is to present an approach for generating coarse resolution (MODIS data) fraction images of forested region in Korea peninsula using forest type area fraction derived from high resolution data (ASTER data) in regional forest area. A 15-m spatial resolution multi-spectral ASTER image was acquired under clear sky conditions on September 22, 2003 over the forested area near Seoul, Korea and was used to select each end-member that represent a pure reflectance of component of forest such as different forest, bare soil and water. The area fraction of selected each end-member and a 500-m spatial resolution MODIS reflectance product covering study area was applied to a linear mixture inversion model for calculating the fraction image of forest component across the South Korea. We found that the area fraction values of each end-member observed from high resolution image data could be used to separate forest cover in low resolution image data.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.103-114
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• 2024

The escalating demands for high-resolution satellite imagery necessitate the dissemination of geospatial data with superior accuracy.Achieving precise positioning is imperative for mitigating geometric distortions inherent in high-resolution satellite imagery. However, maintaining sub-pixel level accuracy poses significant challenges within the current technological landscape. This research introduces an approach wherein upsampling is employed on both the satellite image and ground control points (GCPs) chip, facilitating the establishment of a high-resolution satellite image precision sensor orientation. The ensuing analysis entails a comprehensive comparison of matching performance. To evaluate the proposed methodology, the Compact Advanced Satellite 500-1 (CAS500-1), boasting a resolution of 0.5 m, serves as the high-resolution satellite image. Correspondingly, GCP chips with resolutions of 0.25 m and 0.5 m are utilized for the South Korean and North Korean regions, respectively. Results from the experiment reveal that concurrent upsampling of satellite imagery and GCP chips enhances matching performance by up to 50% in comparison to the original resolution. Furthermore, the position error only improved with 2x upsampling. However,with 3x upsampling, the position error tended to increase. This study affirms that meticulous upsampling of high-resolution satellite imagery and GCP chips can yield sub-pixel-level positioning accuracy, thereby advancing the state-of-the-art in the field.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.87-98
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• 2000

Image merging techniques have been developed to integrate the advantage of different data type. The objective of this study is to present the optimal method for merging high spatial resolution panchromatic image, such as the latest commercial satellite data, and low spatial resolution mulitspectral images. For this study, a set of 2m resolution panchromatic and 8m resolution mulitspectral data were simulated by using airborne mulitspectral data. Five merging methods of MWD, IHS, PCA, HPF, and CN were applied to produce four bands of high spatial resolution mulitspectral data. Merging results were evaluated by visual interpretation, image statistics, semivariogram, and spectral characteristics. From the aspects of both spatial resolution and spectral information, the wavelet-based MWD merging method have shown very similar results compared with the original data used for the merging.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.169-178
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• 2001

The demand for high-resolution images is gradually increasing, whereas many imaging systems have been designed to allow a certain level of aliasing during image acquisition. Thus, digital image processing approaches have recently been investigated to reconstruct a high-resolution image from aliased low-resolution images. However, since the sub-pixel motion information is assumed to be accurate in most conventional approaches, the satisfactory high-resolution image cannot be obtained when the sub-pixel motion information is inaccurate. Therefore, in this paper we propose a new algorithm to reduce the distortion in the reconstructed high-resolution image due to the inaccuracy of sub-pixel motion information. For this purpose, we analyze the effect of inaccurate sub-pixel motion information on a high-resolution image reconstruction, and model it as zero-mean additive Gaussian errors added respectively to each low-resolution image. To reduce the distortion we apply the modified multi-channel image deconvolution approach to the problem. The validity of the proposed algorithm is both theoretically and experimentally demonstrated in this paper.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.525-527
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• 2003

Recently, the spatial resolution of earth observation satellites is significantly increased to a few meters. Such high spatial resolution images definitely will provide lots of information for detail-thirsty remote sensing users. However, it is more difficult to develop automated image algorithms for automated image feature extraction and pattern recognition. In this study, we propose a two-stage procedure to extract road information from high resolution satellite images. At first stage, a watershed segmentation technique is developed to classify the image into various regions. Then, a knowledge is built for road and used to extract the road regions. In this study, we use panchromatic and multi-spectral images of the IKONOS satellite as test dataset. The experiment result shows that the proposed technique can generate suitable and meaningful road objects from high spatial resolution satellite images. Apparently, misclassified regions such as parking lots are recognized as road needed further refinement in future research.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1393-1401
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• 2014

This paper presents an adaptive image interpolation method using a pixel-based neighbor embedding which is modified from the patch-based neighbor embedding of contemporary super resolution algorithms. Conventional interpolation methods for high resolution detect at least 16-directional edges in order to remove zig-zaging effects and selectively choose the interpolation strategy according to the direction and value of edge. Thus, they require much computation and high complexity. In order to develop a simple interpolation method preserving edge's directional shape, the proposed algorithm adopts the simplest Haar wavelet and suggests a new pixel-based embedding scheme. First, the low-quality image but high resolution, magnified into 1 octave above, is acquired using an adaptive 8-directional interpolation based on the high frequency coefficients of the wavelet transform. Thereafter, the pixel embedding process updates a high resolution pixel of the magnified image with the weighted sum of the best matched pixel value, which is searched at its low resolution image. As the results, the proposed scheme is simple and removes zig-zaging effects without any additional process.

• Current Industrial and Technological Trends in Aerospace
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• v.6 no.2
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• pp.90-98
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• 2008

In recent, image data compression method of high resolution satellite image is the important issue for its application and development. The CCSDS (Consultative Committee for Space Data Systems) published a standard for the high resolution image data compression, CCSDS 122.0-B-1, in the end of 2005, which is expected to be widely applied in process of compression for the high resolution satellite images. In this paper, it is explained that the current trends of image compression methods for high resolution satellites, and then the comparison results between CCSDS 122.0-B-1 standard and JPEG are described.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.72-75
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• 2008

Image segmentation techniques becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Seeded Region Growing (SRG) and Edge Information. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying SRG. Finally the region merging process, using region adjacency graph (RAG), was carried out to get the final segmentation result. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.85-92
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• 2022

In this paper, we propose a technique that can express low-resolution hair and fur simulations in high-resolution without noise using ConvNet and geometric images of strands in the form of lines. Pairs between low-resolution and high-resolution data can be obtained through physics-based simulation, and a low-resolution-high-resolution data pair is established using the obtained data. The data used for training is used by converting the position of the hair strands into a geometric image. The hair and fur network proposed in this paper is used for an image synthesizer that upscales a low-resolution image to a high-resolution image. If the high-resolution geometry image obtained as a result of the test is converted back to high-resolution hair, it is possible to express the elastic movement of hair, which is difficult to express with a single mapping function. As for the performance of the synthesis result, it showed faster performance than the traditional physics-based simulation, and it can be easily executed without knowing complex numerical analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3942-3961
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• 2019

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ${\times}8$, our method performs favorably against other methods in terms of gradients similarity.