• Korean Journal of Remote Sensing
• /
• v.30 no.2
• /
• pp.185-205
• /
• 2014

SAR ground moving target indicator (GMTI) has long been an important issue for SAR advanced applications. As spatial resolution of space-borne SAR system has been significantly improved recently, the GMTI becomes a very useful tool. Various GMTI techniques have been developed particularly using multi-channel SAR systems. It is, however, still problematic to detect ground moving targets within single channel SAR images while it is not practical to access high resolution multi-channel space-borne SAR systems. Once a ground moving target is detected, it is possible to retrieve twodimensional velocities of the target from single channel space-borne SAR with an accuracy of about 5 % if moving faster than 3 m/s. This paper presents a quick-and-dirty method for detecting ground moving targets from single channel SAR single-look complex (SLC) images by differentiation. Since the signal powers of derivatives present Doppler centroid and rate, it is very efficient and effective for detection of non-stationary targets. The derivatives correlate well with velocities retrieved by a precise method with a correlation coefficient $R^2$ of 0.62, which is well enough to detect the ground moving targets. While the approach is theoretically straightforward, it is necessary to remove the effects of residual Doppler rate before finalizing the ground moving target candidates. The confidence level of results largely depends on the efficiency and effectiveness of the residual Doppler rate removal method. Application results using TerraSAR-X and truck-mounted corner reflectors validated the efficiency of the method. While the derivatives of moving targets remain easily detectable, the signal energy of stationary corner reflectors was suppressed by about 18.5 dB. It results in an easy detection of ground targets moving faster than 8.8 km/h. The proposed method is applicable to any high resolution single channel SAR systems including KOMPSAT-5.

• Korean Journal of Remote Sensing
• /
• v.40 no.4
• /
• pp.397-418
• /
• 2024

The Land Surface Temperature (LST) is a crucial parameter in identifying drought. It is essential to identify how LST can increase its accuracy, particularly in mountainous and hill areas. Increasing the LST accuracy can be achieved by applying early data processing in the correction phase, specifically in the context of topographic correction on the Lambertian model. Empirical evidence has demonstrated that this particular stage effectively enhances the process of identifying objects, especially within areas that lack direct illumination. Therefore, this research aims to examine the application of the Lambertian model in estimating LST using the Multi-Channel Method (MCM) across various physiographic regions. Lambertian model is a method that utilizes Lambertian reflectance and specifically addresses the radiance value obtained from Sun-Canopy-Sensor(SCS) and Cosine Correction measurements. Applying topographical adjustment to the LST outcome results in a notable augmentation in the dispersion of LST values. Nevertheless, the area physiography is also significant as the plains terrain tends to have an extreme LST value of ≥ 350 K. In mountainous and hilly terrains, the LST value often falls within the range of 310-325 K. The absence of topographic correction in LST results in varying values: 22 K for the plains area, 12-21 K for hilly and mountainous terrain, and 7-9 K for both plains and mountainous terrains. Furthermore, validation results indicate that employing the Lambertian model with SCS and Cosine Correction methods yields superior outcomes compared to processing without the Lambertian model, particularly in hilly and mountainous terrain. Conversely, in plain areas, the Lambertian model's application proves suboptimal. Additionally, the relationship between physiography and LST derived using the Lambertian model shows a high average R² value of 0.99. The lowest errors(K) and root mean square error values, approximately ±2 K and 0.54, respectively, were achieved using the Lambertian model with the SCS method. Based on the findings, this research concluded that the Lambertian model could increase LST values. These corrected values are often higher than the LST values obtained without the Lambertian model.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.3
• /
• pp.205-210
• /
• 2022

A novel OLED pixel circuit is proposed in this paper that uses only n-type thin-film transistors(TFTs) to improve the luminance non-uniformity of the AMOLED display caused by the threshold voltage variation of an OLED driving TFT. The proposed OLED pixel circuit is composed of 6 n-channel TFTs and 2 capacitors. The operation of the proposed OLED pixel circuit consists of the capacitor initializing period, threshold voltage sensing period of an OLED·driving TFT, image data voltage writing period, and OLED·emitting period. As a result of SmartSpice simulation, when the threshold voltage of·OLED·driving TFT varies from 1.2 V to 1.8 V, the proposed OLED pixel circuit has a maximum current error of 5.18 % at IOLED = 1 nA. And, when the OLED cathode voltage rises by 0.1 V, the proposed OLED pixel circuit has very little change in the OLED current compared to the conventional OLED pixel circuit. Therefore, the proposed pixel circuit exhibits superior compensation characteristics for the threshold voltage variation of an OLED driving TFT and the rise of the OLED cathode voltage compared to the conventional OLED pixel circuit.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.2
• /
• pp.276-284
• /
• 2020

This paper proposes a new convolutional neural network (CNN) based spectrum sensing technique for cognitive radio communications. The proposed technique determines the existence of the primary user (PU) by using energy detection without any prior knowledge of the PU's signal. In the proposed method, the received signal is high-rate sampled to sense the entire spectrum bands of interest. After that, fast Fourier transform (FFT) of the signal converts the time domain signal to frequency domain spectrum and by stacking those consecutive spectrums, a 2 dimensional signal is made. The 2 dimensional signal is cut by the sensing channel bandwidth and inputted to the CNN. The CNN determines the existence of the primary user. Since there are only two states (existence or non-existence), binary classification CNN is used. The performance of the proposed method is examined through computer simulation and indoor experiment. According to the results, the proposed method outperforms the conventional threshold-based method by over 2 dB.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.7-10
• /
• 2006

Four programs, i.e. TRMM, ADEOS2, ASTER, and ALOS are going on in Japanese Earth Observation programs. TRMM and ASTER are operating well, and TRMM operation will be continued to 2009. ADEOS2 was failed, but AMSR-E on Aqua is operating. ALOS (Advanced Land Observing Satellite) was successfully launched on $24^{th}$ Jan. 2006. ALOS carries three instruments, i.e., PRISM (Panchromatic Remote Sensing Instrument for Stereo Mapping), AVNIR-2 (Advanced Visible and Near Infrared Radiometer), and PALSAR (Phased Array L band Synthetic Aperture Radar). PRISM is a 3 line panchromatic push broom scanner with 2.5m IFOV. AVNIR-2 is a 4 channel multi spectral scanner with 10m IFOV. PALSAR is a full polarimetric active phased array SAR. PALSAR has many observation modes including full polarimetric mode and scan SAR mode. After the unfortunate accident of ADEOS2, JAXA still have plans of Earth observation programs. Next generation satellites will be launched in 2008-2012 timeframe. They are GOSAT (Greenhouse Gas Observation Satellite), GCOM-W and GCOM-C (ADEOS-2 follow on), and GPM (Global Precipitation Mission) core satellite. GOSAT will carry 2 instruments, i.e. a green house gas sensor and a cloud/aerosol imager. The main sensor is a Fourier transform spectrometer (FTS) and covers 0.76 to 15 ${\mu}m$ region with 0.2 to 0.5 $cm^{-1}$ resolution. GPM is a joint project with NASA and will carry two instruments. JAXA will develop DPR (Dual frequency Precipitation Radar) which is a follow on of PR on TRMM. Another project is EarthCare. It is a joint project with ESA and JAXA is going to provide CPR (Cloud Profiling Radar). Discussions on future Earth Observation programs have been started including discussions on ALOS F/O.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.36 no.5
• /
• pp.533-538
• /
• 2012

In this study, we used an $O_2$-sensitive luminescent dye to measure the $O_2$ concentration of culture media around HeLa cells cultured in a microchannel. $[Ru(bpy)_3]^{2+}$, which dissolves easily in water and which has no phototoxic effect, was used as the $O_2$-sensitive dye. The ratiometric sensing method was applied by introducing calcein as the $O_2$-insensitive dye, in order to overcome the disadvantages of intensity-based sensing. By performing calibration with an amperometric $O_2$ sensor, we could calculate the exact concentration of $O_2$ in the culture media. We applied this technique to measure the $O_2$ concentration around the cultured cells in the microchannel. As expected, the $O_2$ concentration gradually decreased as the cells moved farther away from the channel. This method is expected to be applicable to the investigation of hypoxia, which occurs commonly in scaffolds.

• Current Optics and Photonics
• /
• v.7 no.3
• /
• pp.237-247
• /
• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1631-1645
• /
• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.526-529
• /
• 2007

Due to its inherent feature of high-resolution satellite, there is strong need in some specific area to minimize the processing time required to get a standard image on hand from downlink signal acquisition. However, in general image processing system, it takes considerable time to get image data up to certain level from raw data acquisition because the huge amount of data is dealt sequentially as input data. This paper introduces the high-speed image processing system which generates the image data only for the area selected by user. To achieve the high speed performance, this system includes Quick Look Image display function with sampling, ROI selection function, Image Line Index function, and Distributed processing function. The developed RPS was applied to KOMPSAT-2 320Mbps downlink channel and its effectiveness was successfully demonstrated. This feature to provide the image product very quickly is expected to promote the application of high resolution satellite image.

• Proceedings of the KSRS Conference
• /
• 1998.09a
• /
• pp.225-230
• /
• 1998

The purpose of this study is to contribute the development of an El Nino prediction model. The objectives of the study are to (1) extract sea surface height data from the TOPEI/Poseidon altimeter, and (2) compare the relations among the sea surface height, sea surface temperature and wind field. NOAA AVHRR Multi-channel data is used for sea surface temperature and wind data is derived from ERS 1, 2 AMI wind scatterometer. The results showed that sea surface height has increased significantly during the El Nino season. The sea surface height is positively related to sea surface temperature and negatively related to zonal wind.