• Korean Journal of Remote Sensing
• /
• v.19 no.3
• /
• pp.191-200
• /
• 2003

Remote sensing division of satellite technology research center (SaTReC) , Korea advanced institute of science and technology (KAIST) has developed a ground receiving and processing system for high resolution satellite images. The developed system will be adapted and operated to receive, process and distributes images acquired from of the second Korean Multi-purpose Satellite (KOMPSAT-2), which will be launched in 2004. This project had initiated to develop and Koreanize the state-of-the-art technologies for the ground receiving system for high resolution remote sensing images, which range from direct ingestion of image data to the distribution of products through precise image correction. During four years development from Dec. 1998 until Aug. 2002, the system had been verified in various ways including real operation of custom-made systems such as a prototype system for SPOT and a commercialized system for KOMPSAT-1. Currently the system is under customization for installation at KOMPSAT-2 ground station. In this paper, we present accomplished work and future work.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.331-336
• /
• 1999

The optical sensors of Electro-Optical Camera (EOC) and Ocean Scanning Multi-spectral Imager (OSMI) aboard the Korea Multi-Purpose SATellite (KOMPSAT) will be placed in a sun synchronous orbit in 1999. The EOC and OSMI sensors are expected to produce the land mapping imagery of Korean territory and the ocean color imagery of world oceans, respectively. Utilization of the EOC and OSMI data would encompass the various fields of science and technology such as land mapping, land use and development, flood monitoring, biological oceanography, fishery, and environmental monitoring. Readiness of data support for user community is thus essential to the success of the KOMPSAT program. As part of testing such readiness prior to the KOMPSAT launch, we have performed the preliminary acceptance test for the KOMPSAT data processing system using the simulated EOC and OSMI data sets. The purpose of this paper is to demonstrate the readiness of the KOMPSAT data processing system, and to help data users understand how the KOMPSAT EOC and OSMI data are processed and archived. Test results demonstrate that all requirements described in the data processing specification have been met, and that the image integrity is maintained for all products. It is however noted that since the product accuracy is limited by the simulated sensor data, any quantitative assessment of image products can not be made until actual KOMPSAT images will be acquired.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1418-1420
• /
• 2003

This presentation summarizes the development of the integration information search system for a Test-bed area located in Daejeon. It will be used for the validation of software components developed for the high resolution satellite image processing. The system development utilizes the Java programming language and implements the web browse capabilities to search, manage, and augment the satellite image data, the Ground Control Point(GCP) data, the spectral information on land cover types, the atmospheric data, and the topographical map.

• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.578-581
• /
• 2006

Usually to achieve precise geolocation of satellite images, we need to get GCPs (Ground control points) from individual scenes. This requirement greatly increases the cost and processing time for satellite mapping. In this article, we focus on finding appropriate sensor models for entire image strips composing of several adjacent scenes. We tested the feasibility of modelling whole satellite image strips by establishing sensor models of one scene with GCPs and by applying the models to neighboring scenes without GCPs. For this, we developed two types of sensor models: collinearity-based type and orbit-based type and tested them using different sets of unknowns. Results indicated that although the performance of two types was very similar, for modelling individual scenes, it was not for modelling the whole strips. Moreover, the performance of sensor models was remarkably sensitive to different sets of unknowns. It was found that the orbit-based model using attitude biases as unknowns can be used to model SPOT image strips of 420 Km in length.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.804-809
• /
• 2002

Remote sensing division of satellite technology research center (SaTReC), Korea advanced institute of science and technology (KAIST) has developed a ground receiving and processing system for high resolution satellite images. Developed system will be adapted and operated to receive, process and distributes images acquired from of the second Korean Multi-purpose Satellite (KOMPSAT-2), which will be launched in 2004. This project had initiated to develop and Koreanize the state-of-the-art technologies related to the ground receiving system fur high resolution remote sensing images, which range from direct ingestion of image data to the distribution of products through precise image correction. During four years development, the system has been verified in various ways including real operation of custom-made systems such as a prototype system for SPOT and a commercialised system for KOMPSAT-1. Currently the system is under customisation for installation at KOMPSAT-2 ground station. In this paper, we present accomplished work and future work.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.458-460
• /
• 2003

This paper proposes new algorithms for cloudy area detection by GHA (Generalized Hebbian Algorithm) and SOFM (Self-Organized Feature Map). SOFM and GHA are unsupervised neural networks and are used for pattern classification and shape detection of satellite image. Proposed algorithm is based on block based image processing that size is 16${\times}$16. Results of proposed algorithm shows good performance of cloudy area detection except blur cloudy area.

• Korean Journal of Remote Sensing
• /
• v.31 no.3
• /
• pp.245-253
• /
• 2015

According to wide civilian utilization of multi sensor satellite information, practical needs for fusion processing and interoperable operation with multiple remote sensing imageries within distributed remote server are being increased. For this task, OGC standards with respect to satellite images and its derived products are crucial factors. This study is to present an applicability of WPS through testing implementation of image processing algorithm. Open sources such as Geoserver and OTB were used linked to WPS application for implementation. WPS can be solely used for web service supporting geoprocessing algorithm, but technical consideration compromising with other important standard protocols including WMS, WFS, WCS, or WMTS is necessary to build full featured geo web for remote sensing imageries. It is expected that application of these international standards for geo-spatial information is an important approach to produce value-added results by interoperable processing between interorganizations or information dissemination containing practical satellite image processing functionalities.

• Journal of Korea Multimedia Society
• /
• v.19 no.4
• /
• pp.725-735
• /
• 2016

High-resolution satellite images are used in the fields of mapping, natural disaster forecasting, agriculture, ocean-based industries, infrastructure, and environment, and there is a progressive increase in the development and demand for the applications of high-resolution satellite images. Users of the satellite images desire accurate quality of the provided satellite images. Moreover, the distinguishability of each image captured by an actual satellite varies according to the atmospheric environment and solar angle at the captured region, the satellite velocity and capture angle, and the system noise. Hence , NIIRS must be measured for all captured images. There is a significant deficiency in professional human resources and time resources available to measure the NIIRS of few hundred images that are transmitted daily. Currently, NIIRS is measured every few months or even few years to assess the aging of the satellite as well as to verify and calibrate it [3]. Therefore, we develop an algorithm that can measure the national image interpretability rating scales (NIIRS) of a typical satellite image rather than an artificial target satellite image, in order to automatically assess its quality. In this study, the criteria for automatic edge region extraction are derived based on the previous works on manual edge region extraction [4][5], and consequently, we propose an algorithm that can extract the edge region. Moreover, RER and H are calculated from the extracted edge region for automatic edge region extraction. The average NIIRS value was measured to be 3.6342±0.15321 (2 standard deviations) from the automatic measurement experiment on a typical satellite image, which is similar to the result extracted from the artificial target.

• Korean Journal of Remote Sensing
• /
• v.38 no.2
• /
• pp.167-177
• /
• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.

• Journal of Satellite, Information and Communications
• /
• v.11 no.2
• /
• pp.14-17
• /
• 2016

This paper describes the implementation of automatic number-braille converter based on image processing for the blind people. The algorithm is consists of four main steps. First step is binary image conversion of the input image obtained by the camera. the second step is segmentation operation by means of dilation and labelling of the character. Next step is calculation of cross-correlation between segmented text image and pre-defined text-pattern image. The final step is generation of brail output which is relevant to input image. The computer simulation result was showing 91.8% correct conversion rate for arabian numbers which is printed in A4-sheet and practical possibility was also confirmed by using implemented automatic number-braille converter based on DSP image processing board.