• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.427-436
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• 2008

This paper presents radar remote sensing of soil moisture and surface roughness for vegetated surfaces. A precise volume scattering model for a vegetated surface is derived based on the first-order radiative transfer technique. At first, the scattering mechanisms of the scattering model are analyzed for various conditions of the vegetation canopies. Then, the scattering model is simplified step by step for developing an appropriate inversion algorithm. For verifying the scattering model and the inversion algorithm, the polarimetric backscattering coefficients at 1.85 GHz, as well as the ground truth data, of a tall-grass field are measured for various soil moisture conditions. The genetic algorithm is employed in the inversion algorithm for retrieving soil moisture and surface roughness from the radar measurements. It is found that the scattering model agrees quite well with the measurements. It is also found that the retrieved soil moisture and surface roughness parameters agree well with the field-measured ground truth data.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.4
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• pp.297-305
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• 2021

Monocular depth estimation helps the robot to understand the surrounding environments in 3D. Especially, deep-learning-based monocular depth estimation has been widely researched, because it may overcome the scale ambiguity problem, which is a main issue in classical methods. Those learning based methods can be mainly divided into three parts: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning trains the network from dense ground-truth depth information, unsupervised one trains it from images sequences and semi-supervised one trains it from stereo images and sparse ground-truth depth. We describe the basics of each method, and then explain the recent research efforts to enhance the depth estimation performance.

• Korean Journal of Logic
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• v.16 no.2
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• pp.131-154
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• 2013

Michael Williams, in his paper, rejects the wide-accepted view that Donald Davidson's radical interpretation is a truth conditional account of meaning, and suggests a claim that robust truth in fact does not play any role in Davidson's interpretation and thus interpretation can be in accord with the deflationary theory of truth. In this paper, I will first research the right understanding on the explanatory relations in radical interpretation between truth and meaning, and on that ground, will evaluate the adequacy of Williams' suggestion. My diagnosis is that the acceptability of Williams' idea depends on how we regard the several factors which are crucial for interpretation. Especially I will argue that whether truth condition is regarded as deflationary or inflationary makes difference to the way of understanding radical interpretation, hence the room for taking radical interpretation as deflationary can be in two different ways. Furthermore I will show the same argument can be applied to Williams' another claim that Paul Horwich's use theory of meaning is similar to Davidson's account.

• Smart Structures and Systems
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• v.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Journal of IKEEE
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• v.17 no.1
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• pp.1-9
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• 2013

The quantitative evaluation of DEM(Digital Elevation Map) is very important to the assessment of the effectiveness for the applied 3D image analysis technique. This paper presents a new quantitative evaluation method of 3D reconstruction process by using synthetic images. The proposed method is based on the assumption that a preacquired DEM and ortho-image should be the pseudo ground truth. The proposed evaluation process begins by generating a pair of photo-realistic synthetic images of the terrain from any viewpoint in terms of application of the constructed ray tracing algorithm to the pseudo ground truth. By comparing the DEM obtained by a pair of photo-realistic synthetic images with the assumed pseudo ground truth, we can analyze the quantitative error in DEM and evaluate the effectiveness of the applied 3D analysis method. To verify the effectiveness of the proposed evaluation method, we carry out the quantitative and the qualitative experiments. For the quantitative experiment, we prove the accuracy of the photo-realistic synthetic image. Also, the proposed evaluation method is experimented on the 3D reconstruction with regards to the change of the matching window. Based on the fact that the experimental result agrees with the anticipation, we can qualitatively manifest the effectiveness of the proposed evaluation method.

• Defense and Technology
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• no.3 s.229
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• pp.42-53
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• 1998

가상시험장은 양질의 시험 서비스를 보다 적은 비용으로 신속하게 제공하면서 실제 시험시에 발생할 수 있는 새로운 시험공간의 확보, 안전문제, 오염방지, 민원등에 효과적으로 대처할 수 있는 기술로 판단된다. 가상시험장의 효용가치는 가상시험 결과와 실제시험 결과의 근접성에 의해 결정되므로 모델링과 시뮬레이션 기술 개발과 더불어 Ground Truth Data의 축적이 선행되어야 성공적인 가상시험장 구축을 기대할 수 있다.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.746-749
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• 2002

This paper is introducing a radio control helicopter as a new platform of ground truth measurement. This helicopter is normally used for spraying an agricultural chemical. It can do pinpoint hovering and programing flight using DGPS etc., A spectrometer with dual port can measure ground surface and white reference plate at the same time. And it can also take digital images by digital camera. It is needed to collect ground reflectance information as satellite sensor footprint size for satellite data validation. Generally it is possible to get such ground reflectance by an airplane measurement. But it is high cost and not so easy to make a measurement by airplane. Developed validation system can provide such ground reflectance in low cost and easy.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.37-51
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• 2023

The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.

• Journal of KIISE
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• v.44 no.3
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• pp.275-286
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• 2017

In this study, we propose a novel technique of software clustering to recover the software module-view by using the dependency and author entropy of modules. The proposed method first performs clustering of modules based on structural and logical dependencies, then it migrates selected modules from the clustered result by utilizing the author entropy of each module. In order to evaluate the proposed method, we calculated the MoJoFM values of the recovery result by applying the method to open-source projects among which ground-truth decompositions are well-known. Compared to the MoJoFM values of previously studied techniques, we demonstrated the effectiveness of the proposed method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.9 s.112
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• pp.804-810
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• 2006

In this paper, we describe a measurement technique for the backscattering coefficient and ground truth of a vegetation canopy in detail. A simple microwave backscattering model for vegetation canopies is verified by being compared with this measurement. An R-band$(1.7\sim2.0GHz)$ scatterometer system is used to measure the backscattering coefficient of a vegetated area in the Han-river park for various incidence angles and a wide range of the soil moisture contents. It is found that the model agrees quite well with the measurements for co-polarized radar backscatter, and needs a correction for cross polarized radar backscatter.