• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.165-170
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• 1999

Previous technology of remote sensing was focused on analyzing raster image and gaining information through image processing. But now it has extended to diverse fields like automatic map generation, material exploitation or monitoring environmental changes with effort to utilizing practical usage. And with rapid expansion of information exchange on Internet and high-speed network, the demand of public which want to utilize remotely sensed image has been increased. This makes growth of service on acquisition and processing remotely sensed image. GeoNet is a Java-based remotely sensed image processing system. It is based on Java object-oriented paradigm and features cross-platform, web-based execution and extensibility to client/server remotely sensed image processing model. Remotely sensed image processing software made by Java programming language can suggest alternatives to meet readily demand on remotely sensed image processing in proportion to increase of remotely sensed data. In this paper, we introduce GeoNet and explain its architecture.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.736-739
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• 2006

While hyperspectral data are very rich in information, their processing poses several challenges such as computational requirements, noise removal and relevant information extraction. In this paper, the application of advanced scale-space filtering to selected hyperspectral bands was investigated. In particular, a pre-processing tool, consisting of anisotropic diffusion and morphological leveling filtering, has been developed, aiming to an edge-preserving smoothing and simplification of hyperspectral data, procedures which are of fundamental importance during feature extraction and object detection. Two scale space parameters define the extent of image smoothing (anisotropic diffusion iterations) and image simplification (scale of morphological levelings). Experimental results demonstrated the effectiveness of the developed scale space filtering for the enhancement and smoothing of hyperspectral remote sensing data and their advantage against watershed over-segmentation problems and edge detection.

• Journal of Information Processing Systems
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• 제16권4호
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• pp.795-808
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• 2020

Due to the poor evaluation indexes such as detection accuracy and recall rate when Yolov3 network detects aircraft in remote sensing images, in this paper, we propose a remote sensing image aircraft detection method based on machine vision. In order to improve the target detection effect, the Inception module was introduced into the Yolov3 network structure, and then the data set was cluster analyzed using the k-means algorithm. In order to obtain the best aircraft detection model, on the basis of our proposed method, we adjusted the network parameters in the pre-training model and improved the resolution of the input image. Finally, our method adopted multi-scale training model. In this paper, we used remote sensing aircraft dataset of RSOD-Dataset to do experiments, and finally proved that our method improved some evaluation indicators. The experiment of this paper proves that our method also has good detection and recognition ability in other ground objects.

• 대한원격탐사학회지
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• 제26권6호
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• pp.693-703
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• 2010

High spectral resolution of hyperspectral data enables analysis of complex natural phenomena that is reflected on the data nonlinearly. Although many manifold learning methods have been developed for such problems, most methods do not consider the spatial correlation between samples that is inherent and useful in remote sensing data. We propose a manifold learning method which directly combines the spatial proximity and the spectral similarity through kernel PCA framework. A gain factor caused by spatial proximity is first modelled with a heat kernel, and is added to the original similarity computed from the spectral values of a pair of samples. Parameters are tuned with intelligent grid search (IGS) method for the derived manifold coordinates to achieve optimal classification accuracies. Of particular interest is its performance with small training size, because labelled samples are usually scarce due to its high acquisition cost. The proposed spatial kernel PCA (KPCA) is compared with PCA in terms of classification accuracy with the nearest-neighbourhood classification method.

• 대기
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• 제17권2호
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• pp.125-133
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• 2007

Cloud amount calculation algorithm was developed using MTSAT-1R satellite data. The cloud amount is retrieved at 5 km ${\times}$ 5 km over the Korean Peninsula and adjacent sea area. The algorithm consists of three steps that are cloud detection, cloud type classification, and cloud amount calculation. At the first step, dynamic thresholds method was applied for detecting cloud pixels. For using objective thresholds in the algorithm, sensitivity test was performed for TBB and Albedo variation with temporal and spatial change. Detected cloud cover was classified into 3 cloud types (low-level cloud, cirrus or uncertain cloud, and cumulonimbus type high-level cloud) in second step. Finally, cloud amount was calculated by the integration method of the steradian angle of each cloud pixel over $3^{\circ}$ elevation. Calculated cloud amount was compared with measured cloud amount with eye at surface observatory for the validation. Bias, RMSE, and correlation coefficient were 0.4, 1.8, and 0.8, respectively. Validation results indicated that calculated cloud amount was a little higher than measured cloud amount but correlation was considerably high. Since calculated cloud amount has 5km ${\times}$ 5km resolution over Korean Peninsula and adjacent sea area, the satellite-driven cloud amount could show the possibility which overcomes the temporal and spatial limitation of measured cloud amount with eye at surface observatory.

• 대기
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• 제15권3호
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• pp.167-178
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• 2005

Using the FSSP-100(FSSP) and Microwave Radiometer (MWR), the fog and clear day characteristics (the size and number concentration of fog particles and the liquid water content) have been measured and analyzed at Daegwallyoung observation site ($37^{\circ}41^{\prime}N$, $128^{\circ}45^{\prime}E$) during 27 - 30 November 2003 (fog day) and 19 January 2004 (clear day). During the fog days, the measured fog-particle size by using FSSP is 0.8~8.4 ${\mu}m$, which is similar to the WMO typical value, the fog number concentration varies from 121 to 200 count ($No./cm^2$) and the fog liquid water content from $0.018g/m^3-0.1g/m^3$ in the site. The precipitable water vapor obtained by the MWR, showing the correlation coefficient $R^2$=0.83 between the total precipitable water vapor obtained from the radio sonde and MWR, shows the larger amount (0.75-8.3 cm) during the fog days than the clear-sky data (0.2 cm).

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2005년도 춘계학술발표회
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• pp.1072-1073
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• 2005

• 대한원격탐사학회지
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• 제33권1호
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• pp.15-24
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• 2017

Jellyfish (cnidarian) are conspicuous in many marine ecosystems when in bloom. Despite their importance for the ecosystem structure and function, very few sampling programs are dedicated to sample jellyfish because they are patchily distributed and easily clogged plankton net. Although satellite remote sensing is an excellent observing tool for many phenomena in the ocean, their uses for monitoring jellyfish are not possible due to the coarse spatial resolutions. Hence, we developed the low altitude remote sensing platform to detect jellyfish in high resolutions, which allow us to monitor not only horizontal, but also vertical migration of them. Using low altitude remote sensing platform,we measured the jellyfish from the pier at the Chesapeake Biological Laboratory in Chesapeake Bay. The patterns observed included discrete patches, in rows that were aligned with waves that propagated from deeper regions, and aggregation around physical objects. The corresponding areas of exposed jellyfish on the sea surface were $0.1{\times}10^4pixel^2$, $0.3{\times}10^4pixel^2$, and $2.75{\times}10^4pixel^2$, respectively. Thus, the research result suggested that the migration of the jellyfish was related to the physical forcing in the sea surface.

• ETRI Journal
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• 제31권3호
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• pp.348-350
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• 2009

To fit the limited volume and power consumption of the passive radio frequency transmission system of the electronic time fuze, an improved coded mark inversion (CMI) is proposed in this letter. From the performance analysis, the energy transmission efficiency of this encoding method is at least 50% higher than that of CMI and NRZ. Finally, the experiment results show that by adopting this improved CMI, the change of DC voltage through magnetic coupling is lower than 0.2 V when the accuracy of data transmission is above 99.5%.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회(1)
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• pp.542-543
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• 2005