Earticle

본 연구의 목적은 컴퓨터 방사선영상에서 X-선 초점 크기와 디지털영상후처리에 따른 공간분해능을 정량적 으로 분석하고자 하였다. 초점의 크기는 소초점(0.6 mm)와 대초점(1.2 mm)을 이용하였다. 공간분해능의 정량적 분석 은 엣지 측정법의 변조전달함수(MTF)를 이용하였다. 디지털영상후처리는 다단계 이미지 대비 증폭 알고리즘을 이용 하여 경계면 증강과 대조도 증강에 따른 50%와 10%의 MTF를 평가하였다. 그 결과 모든 초점에서 MTF 50%의 공 간분해능이 대조도 증강보다 경계면 증강에서 유의하게 높았다. 또한 대초점에서 획득된 영상은 디지털영상처리를 통해 공간분해능이 향상되었다. 결론적으로 이러한 결과는 컴퓨터 방사선영상에서 골격계 및 흉부영상과 같은 고 공 간분해능 임상영상을 얻기 위한 기초자료로 활용할 수 있다.

The aim of the present study was to carry out quantitative analysis of spatial resolution for the influence of the focus size and digital image post-processing on the Computed Radiography (CR). The modulation transfer functions of an edge measuring method (MTF) was used for the evaluation of the spatial resolution. The focus size of X-ray tube was used the small focus (0.6 mm) and the large focus (1.2 mm). We evaluated the 50% and 10% of MTF for the enhancement of edge and contrast by using multi-scale image contrast amplification (MUSICA) in digital image post-processing. As a results, the edge enhancement than the contrast enhancement were significantly higher the spatial resolution of MTF 50% in all focus. Also the spatial resolution of the obtained images in a large focus were improved by digital image processing. In conclusion, the results of this study should serve as a basic data for obtain the high resolution clinical images, such as skeletal and chest images on the CR.

연구목적: 이상기후와 도시화로 인한 침수 피해 증가에 따라 드론 기반 고해상도 지형 모델이 홍수 예측 의 핵심 기술로 주목받고 있다. 본 연구는 용인 금학천에서 0.1m급 DSM을 구축하여 공간 해상도가 침 수 범위와 양상에 미치는 영향을 분석하고, 예측 정확성 향상을 위한 기초 자료를 제공하는 것을 목표로 한다. 연구방법: 드론과 첨단 기술을 활용해 금학천의 고해상도 DSM과 정사영상을 구축하고, 다양한 해상도의 DSM을 생성해 침수 범위를 정밀 분석하였다. 이를 위해 항공사진측량, 지상기준점 설정, 3D 모델링을 적용하고 리샘플링 기법으로 다중 해상도 데이터를 확보하였다. 연구결과: 7단계 공간 해상 도의 DSM을 기반으로 하천 수위 상승에 따른 침수면적 변화를 분석한 결과, 해상도가 낮아질수록 침수 면적 차이가 커지는 경향을 확인하였다. 결론: 이를 통해 침수 해석에서 공간 해상도 선택 시 계산 효율 성과 오차율 간의 균형을 고려해 최적의 해상도를 선정하는 것이 중요함을 제안하였다.

Purpose: With increasing flood damage from climate change and urbanization, drone-based highresolution terrain models are crucial for urban flood prediction. This study builds a 0.1m-resolution DSM of Geum-Hak stream, Yongin, analyzing the impact of spatial resolution on flood patterns to improve prediction accuracy and applications. Method: Using drones and advanced technologies, high-resolution DSMs and orthophotos were created for the Geum-Hak cheon area. Various DSM resolutions were generated to analyze flood damage, applying aerial photogrammetry, GCP setting, 3D modeling, and resampling techniques for accurate terrain data. Result: The analysis of inundated area changes based on the rise in river water level, using DSMs with seven different spatial resolutions, revealed that as the resolution decreased, the differences in the flooded area increased. Conclusion: Based on these findings, it is suggested that when selecting spatial resolution for flood analysis, a balance between computational efficiency and error rate should be carefully considered to determine the optimal resolution.

Background: The conventional cerium-doped Gd2Al2Ga3O12 (GAGG(Ce)) scintillator-based gamma-ray imager has a bulky detector, which can lead to incorrect positioning of the gammaray source if the shielding against background radiation is not appropriately designed. In addition, portability is important in complex environments such as inside nuclear power plants, yet existing gamma-ray imager based on a tungsten mask tends to be weighty and therefore difficult to handle. Motivated by the need to develop a system that is not sensitive to background radiation and is portable, we changed the material of the scintillator and the coded aperture. Materials and Methods: The existing GAGG(Ce) was replaced with Bi4Ge3O12 (BGO), a scintillator with high gamma-ray detection efficiency but low energy resolution, and replaced the tungsten (W) used in the existing coded aperture with lead (Pb). Each BGO scintillator is pixelated with 144 elements (12 × 12), and each pixel has an area of 4 mm × 4 mm and the scintillator thickness ranges from 5 to 20 mm (5, 10, and 20 mm). A coded aperture consisting of Pb with a thickness of 20 mm was applied to the BGO scintillators of all thicknesses. Results and Discussion: Spectroscopic characterization, imaging performance, and image quality evaluation revealed the 10 mm-thick BGO scintillators enabled the portable gamma-ray imager to deliver optimal performance. Although its performance is slightly inferior to that of existing GAGG(Ce)-based gamma-ray imager, the results confirmed that the manufacturing cost and the system’s overall weight can be reduced. Conclusion: Despite the spectral characteristics, imaging system performance, and image quality is slightly lower than that of GAGG(Ce), the results show that BGO scintillators are preferable for gamma-ray imaging systems in terms of cost and ease of deployment, and the proposed design is well worth applying to systems intended for use in areas that do not require high precision.

This paper presents a robust vehicle detection method for Low Earth Orbit satellite imagery degraded by environmental factors. To enhance feature representation from RGB and IR images, a denoising encoder and a multimodal fusion (MF) module with multi-head squeeze excitation are used. To compensate for texture loss in small vehicles at low resolution, a spatial attention module (SAM) is embedded into the YOLOv5 backbone, and a modified wide activation super-resolution (WDSR) branch is attached for learning finer-grained representations. Experiments on degraded images show that the proposed model outperforms the YOLO series and SuperYOLO in terms of mean average precision (mAP).

최근 디스플레이 기술의 발전으로 인하여 하나의 큰 화면을 여러 영역으로 나누어 사용하거나 여러 화면 기기들을 연결하여 함께 사용하는 경우가 증가하고 있다. 이와 같은 환경에서는 현재 수행 중인 작업과 관련성이 높은 과제 창과 그렇지 않은 비과제 창으로 화면이 나누어질 수 있고, 이때 에너지 효율성을 높이기 위해 비과제 창의 주사율을 선택적으로 줄일 수 있다. 하지만 비과제 창의 주사율 저하가 과제 창의 작업 수행에 영향을 미치는지에 대해서는 아직 충분히 검증되지 않았다. 이에 본 연구는 실험 1과 2에서 각각 점멸 지각 역치 측정 과제와 화질 저하 탐지 과제를 이용하여 비과제 창의 주사율 저하가 과제 창의 시간적, 공간적 해상도에 영향을 미치는지 검증하였다. 실험 결과, 비과제 창의 주사율 저하는 과제 창의 시간적 해상도에는 영향을 미치지만 공간적 해상도에는 영향을 미치지 않는 것으로 드러났다. 이러한 결과는 실제 디스플레이 사용 환경에서 비과제 창의 주사율을 낮추고자 할 때에는 과제 창에서 수행되고 있는 작업의 특성이 고려되어야 한다는 것을 시사한다.

With recent advances in display technology, there is a growing trend in using a large screen divided into multiple windows or connecting multiple displays to use them together. In such scenarios, the screen can be divided into task-relevant and task-irrelevant windows, allowing selective reduction of the refresh rate in the latter to enhance energy efficiency. However, whether the decrease in the refresh rate in the task-irrelevant window affects task performance in the task-relevant window remains unknown. Here, in two experiments, we adopted critical flicker-fusion frequency (CFF) measurement and image quality degradation detection tasks to investigate the impact of reducing the refresh rate of the task-irrelevant window on the temporal and spatial resolution in the task-relevant window. We found that the refresh rate reduction of the task-irrelevant window affected the temporal, but not the spatial resolution of the task-relevant window. These results suggest that the nature of the current task in the task-relevant window must be considered if the refresh rate in the task-irrelevant window is to be reduced in real-world display usage scenarios.

본 연구는 무인항공 사진측량기법을 이용하여 고해상도 공간정보를 취득하는 것을 목적으로 한다. 실험 대상 지역은 시공현장을 선정하였으며, 무인항공 사진촬영시스템은 GPS가 장착된 멀티콥터형 무인항공기와 DSLR 카 메라를 이용하였다. 대상지역에 대공표지를 설치하고 Network GPS 측량방법으로 지상기준점 측량을 실시하였으 며 표정 및 정확도 분석에 이용하였다. 항공사진은 종중복도 70%, 횡중복도 30% 이상을 확보할 수 있도록 비행 고도와 코스를 계획하여 촬영하였으며 사진측량 기법에 의해 공간해상도 2cm의 수치정사영상과 공간해상도 5cm 의 수치표면모형을 취득하였다. 정확도 분석결과, X방향 2.29~3.75cm, Y방향 1.97~2.98cm, Z방향 4.92~8.48cm의 RMSE가 도출되었으며 취득된 고해상도 공간정보를 이용하여 대상지역의 등고선도, 종횡단도, 시계열 분석 등을 수행함으로써 활용성을 분석하였다.

The object of paper is to acquire high-resolution spatial information based UAV photogrammetric method. UAV, GPS-equipped multicopter type, and DSLR camera were used in order to take UAV photography in study area located construction site. air-photo signal plates were installed in study area and ground control survey using network GPS method were applied for orientation and accuracy analysis. The flight plan was considered flight altitude and course to maintain overlap and side-lap, more 70% and 30%, respectively. The result of data was obtained digital ortho image(spatial resolution 2cm) and digital surface model(spatial resolution 5cm). The RMSE of x, y and z axis were calculated using accuracy analysis and application of survey field was observed to process contour map, profile map and calculating earth volume using high-resolution spatial information.

본 연구에서는 변조전달함수(Modulation transfer function, MTF)를 이용한 컴퓨터 방사선영상의 감도 노출 분류에 따른 공간분해능을 평가하여 컴퓨터 방사선영상 획득의 기초자료로 제시하고자 하였다. 본 실험에서는 100 μmm 픽셀의 영상판을 이용하여 엣지법의 MTF를 측정하였다. 방사선 선질은 IEC 61267에서 권고하고 있는 RQA5를 이용하였다. X-선이 조사된 영상판은 감도 노출 분류를 50, 100, 200, 300, 400, 600, 800, 1200으로 각각 설정한 후 디지털 영상화하였다. 최종 획득된 영상들은 공인된 영상분석 프로그램인 image J와 Origin 8.0을 이용하여 MTF 50%와 10%를 구하여 평가하였다. 그 결과 감도 노출 분류 200에서 가장 우수한 MTF 50%(1.979 ± 0.114 lp/mm)와 MTF 10%(3.932 ± 0.041)의 공간주파수를 구하였다. 따라서 골절 등과 같이 높은 공간분해능을 요구하는 질병진단에 유용할 것으로 사료된다.

The purpose of the study was to present basic data to evaluation of the spatial resolution for exposure class(EC) in computed radiography(CR) by using the modulation transfer function(MTF). In this study, MTF was measured the edge method by using image plate(IP) of 100 μmm pixels. A standard beam quality RQA5 based on an international electro-technical commission(IEC) standard was used to perform the X-ray imaging studies. Digital imaging began to set the sensitivity to EC 50, 100, 200, 300, 400, 600, 800, 1200 in X-ray irradiated to IP. The MTF 50% and 10% in the final images was analysis by using an authorized image analysis program the Origin 8.0 and the image J. As a results, the EC 200 was the best spatial resolution at MTF 50% (1.979 ± 0.114 lp/mm) and MTF 10% (3.932 ± 0.041). Therefore, the EC 200 could be useful for the diagnosis of diseases that require high spatial resolution such as fractures.

연구목적: UAV기반의 사진측량은 기존 항공촬영에 비해 비용이 절감될 뿐만 아니라 원하는 시간과 장소 에 대한 고해상도의 데이터를 취득하기 용이하기 때문에, 공간정보 분야에서도 UAV를 활용한 연구가 진행 되고 있다. 본 연구에서는 UAV 기반의 고해상도 영상을 활용하여 토지피복 분류를 수행하고자 하였다. 연구방법: 고해상도 영상의 획득을 위하여 RGB카메라를 사용하였으며, 추가적으로 식생지 역을 정확하게 분류하기 위해서 다중분광 카메라를 사용하여 동일 지역을 추가 촬영하였다. 최종적으로 RGB 및 다중분광 카메라를 이용하여 생성된 정사영상, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix)을 이용하여 대표적인 감독분류기법인 RF(Random Forest)방법을 이용해 총 7개 클래스 에 대해 토지피복분류를 수행하였다. 연구결과: 분류정확도 평가를 위해 오차행렬을 기반으로 한 정확도 평가를 실시하였으며, 정확도 평가 결과 RGB 영상만을 이용한 감독분류결과와 비교하여 제안 방법이 해당 지역 의 클래스를 효과적으로 분류할 수 있음을 확인하였다. 결론: 본 연구에서 제안한 정사영상, 다중분광영상, NDVI, GLCM을 모두 추가한 경우 기 존의 정사영상만을 이용하였을 때 보다 높은 정확도를 나타냈다. 추후 연구로는 추가적인 입력자료의 개발을 통해 분류 정확도를 향상시키고자 한다.

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

본 논문은 단일 영상을 이용하여 초해상도 방법을 수행하기 위해 질감-공간 영역을 분리한 뒤 세부정보를 중심으로 특징을 분류하는 방법을 제안한다. CNN(Convolutional Neural Network) 기반의 초해상도는 세부정 보를 개선하기 위한 특징 추정 과정에서의 복잡한 절차와 중복된 특징 정보의 생성으로 인해 초해상도에서 가장 중요한 기준인 품질 저하가 발생할 수 있다. 제안하는 방법은 절차적 복잡성을 줄이고 중복 특징 정보의 생성을 최소화하여 초해상도 결과의 품질을 개선하기 위해 입력 영상을 질감과 공간의 두 채널로 분리하였다. 질감 채널에 서는 세부정보 복원을 위해 다중스케일로 변환한 영상에 단계별 skip-connection을 적용한 잔차 블록 구조를 적용하여 특징 정제 과정을 수행함으로써 특징 추출을 개선하였고, 공간 채널에서는 평활화된 형태의 특징을 활용 하여 잡음을 제거하고 구조적 특징을 유지하도록 하였다. 제안하는 방법을 이용해 실험한 결과 기존 초해상도 방법 대비 PSNR 및 SSIM 성능 평가에서 향상된 결과를 보여 품질이 개선됨을 확인할 수 있었다.

In this paper, I proposes a method for performing single image super resolution by separating texture-spatial domains and then classifying features based on detailed information. In CNN (Convolutional Neural Network) based super resolution, the complex procedures and generation of redundant feature information in feature estimation process for enhancing details can lead to quality degradation in super resolution. The proposed method reduced procedural complexity and minimizes generation of redundant feature information by splitting input image into two channels: texture and spatial. In texture channel, a feature refinement process with step-wise skip connections is applied for detail restoration, while in spatial channel, a method is introduced to preserve the structural features of the image. Experimental results using proposed method demonstrate improved performance in terms of PSNR and SSIM evaluations compared to existing super resolution methods, confirmed the enhancement in quality.

본 논문은 채널 강조(Channel Attentin)와 공간 강조(Spatial Attention) 방법을 결합한 딥 러닝 기반의 초해상 도 방법을 제안하였다. 초해상도 과정에서 질감, 특징과 같은 주변 픽셀의 변화량이 큰 고주파 성분의 복원이 중요하다. 채널 강조와 공간 강조를 결합한 특징 강조를 이용한 초해상도 방법을 제안하였다. 기존의 CNN(Convolutional Neural Network) 기반의 초해상도 방법은 깊은 네트워크의 학습이 어려우며, 고주파 성분의 강조가 부족하여 윤곽선 이 흐려지거나 왜곡이 발생한다. 문제를 해결하기 위해 스킵-커넥션(Skip Connection)을 적용한 채널 강조와 공간 강조를 결합한 강조 블록과 잔차 블록(Residual Block)을 사용하였다. 방법으로 추출한 강조된 특징 맵을 부-픽셀 컨볼 루션(Sub-pixel Convolution)을 통해 특징맵을 확장하여 초해상도를 진행하였다. 이를 통해 기존의 SRCNN과 비교 하여 약 PSNR는 5%, SSIM은 3% 향상되었으며 VDSR과 비교를 통해 약 PSNR는 2%, SSIM은 1% 향상된 결과를 보였다.

In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.

A novel mechanism model is proposed for the difference between the lake water areas derived from the remote sensing images at high spatial resolution and low spatial resolution. The areas estimated by images at different spatial resolution are calculated theoretically for the elliptical (including circular), triangular and hexagonal areal ground features. Based on the ideal cases, the quantitative rule between the two kinds of area is summarized. According to the theoretical results, the mechanism model is established for the error of the lake surface area. Especially, the surface area of Ebinur Lake derived with SPOT/VEGETATION (VGT), Landsat EM/ETM+ and MODIS are employed to validate the effectiveness and applicability of this model. With the help of the linear regression method, this model can be used to estimate the error between the Ebinur Lake surface areas derived from satellite images at different spatial resolutions, and promote the lake surface area from low spatial resolution imagery. Consequently, this model is helpful for estimating the lake surface area more accurately in a large region, and its effectiveness can be improved with more historical data.

MUSIC algorithm has often used to solve the direction of arrival estimation problem to take advantage of the benefits of beamspace operations, such as reduced computation complexity, reduced sensitivity to system errors, good resolution, reduced bias in the estimation. In this paper, a variant of the beamspace MUSIC algorithm is developed, taking advantage of unitary transformations. In general, the beamspace MUSIC algorithm improves direction of arrival estimation by using reduced order characteristic polynomial rooting though Gaussian column reduction. The unitary beamspace MUSIC algorithm reduces the computational complexity of MUSIC algorithm by using real valued forward and backward, while maintaining the original precision. We use forward backward spatial smoothing technique as the preprocessor of beamspace MUSIC algorithm to recover the reduced rank of the covariance matrix due to the coherence of source signals. Through computer simulations show that the combination of spatial smoothing a beamspace MUSIC can achieve good resolution performance.

Image enhancement is a process of improving the quality of digital image without knowledge about the source of degradation. A method to enhance the quality of given low resolution image is presented in this paper. The enhancement is made in both wavelet and spatial domain. The upsampling scheme is used to enhance the edge information in the spatial domain. To get a sharp high resolution image, the proposed method uses Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) which decompose the input image into four sub-bands denoted by LL, LH, HL and HH. Then the high frequency sub-bands of DWT as well as the input image are interpolated separately by using bicubic interpolation. The high frequency sub-bands produced by SWT are added into the interpolated high frequency sub-bands to correct the estimated high frequency coefficients. After that, the corrected high frequency sub-bands and interpolated input image are combined by using inverse DWT (IDWT). Consequently, the support of filters to model the regularity of natural images is exploited in the wavelet domain. To minimize the reconstruction error, iterative back projection method is employed. The objective and subjective qualities show that the proposed algorithm over-performs the conventional and state-of-the-art image resolution enhancement techniques.

현재 설치된 광섬유의 문제를 모니터링 하는데 가장 널리 사용되는 방법은 Optical Time Domain Reflectometer (OTDR)이다. OTDR는 FTTx 네트워크를 테스트하기 위해 설계된 계측기이며, 전송 손실 및 접속 손실과 같은 광섬 유의 물리적 특성을 평가한다. OTDR을 이용하여 광로상의 문제점을 정확히 파악하기 위해서는 Spatial resolution을 높이는 것이 중요하다. 펄스폭이 두 반사체 사이의 거리 두 배보다 작을 때는 두 반사체에서 반사되는 신호는 상호간 에 겹침 없이 반사되므로 반사되는 신호의 구분이 가능하지만 펄스폭이 두 반사체 사이의 거리 두 배보다 클 때에는 두 반사 펄스가 겹쳐져 반사되는 신호가 구분되지 못한다. 이와 같은 한계를 극복하기 위해서 본 논문에서는 초 분해 능 알고리즘을 적용하여 Spatial resolution 향상 방법을 제안하였으며, 시뮬레이션 결과, 초 분해능 알고리즘 적용 시 에 분해능이 향상 되어 이벤트 구간을 더 정밀하게 분석할 수 있었다.

The optical time domain reflectometer (OTDR) is the most widely used method to monitor problems with currently installed optical fibers. The OTDR is an instrument designed to test the FTTx network and evaluates the physical properties of the fiber, such as transmission loss and connection loss. It is important to improve the spatial resolution in order to accurately grasp the optical path problems by using the OTDR. When the pulse width is less than twice the distance between the two reflectors, the signals reflected from the two reflectors are reflected without overlap, so that the reflected signal can be distinguished. However, when the pulse width is larger than twice the distance between the two reflectors, so that the reflected signal can not be distinguished. In order to overcome these limitations, this paper proposed a method of improving spatial resolution by applying a super resolution algorithm. As a result of the simulation, the resolution is improved when the super resolution algorithm is applied, and the event interval can be analyzed more precisely.

전정색과 다중분광 위성영상으로부터 고해상도 컬러영상을 제작하는 융합기법에 대한 연구가 원격탐사 분야에서 활발하게 진행되어왔다. 기 개발된 많은 영상융합 기법들은 분광대체, 산술병합 그리고 공간영역 등 세 개의 범주로 구분될 수 있다. 본 연구는 기존 고해상도 위성에 비해 공간해상도와 분광해상도가 향상된 WorldView-2 위성에서 제공하는 전정색과 다중분광영상에 적용된 각 범주의 융합영상 결과물 특성을 색상과 공간정보 왜곡 측면에서 분석하였다. 색상정보의 왜곡을 평가하기 위해 평균차, 분산차 및 ERGAS 등의 정량지표를 산출하였고, 공간정보의 왜곡은 시각적 분석에 의해 평가하였다. 본 연구의 결과에 의하면 공간영역에 기초한 융합기법이 다른 범주의 융합에 비해 상대적으로 분광 및 공간정보의 왜곡이 적은 것으로 분석되었다. 따라서 공간영역에 기반한 융합영상은 대축척 주제도 뿐 아니라 온라인 영상지도 제작을 위해 적절한 입력자료로 활용될 것으로 기대된다.

Many image fusion techniques have been developed to produce high spatial-resolution color imagery from panchromatic (PAN) and multispectral (MULTI) satellite imagery. The conventional techniques could be subdivided into three categories such as spectral substitution, arithmetic merging and spatial domain. This research applied fusion techniques, corresponding to each fusion category, to PAN and MULTI scenes of WorldView-2 satellite. Individual fused images were evaluated in terms of spectral and spatial distortion. The study found that fused imagery, based on spatial domain, were superior to those fusion results from spectral substitution and arithmetic merging. It is anticipated that spatial-domain fusion technique would be appropriate in producing fused imagery that could play a role of input data for online image map as well as thematic map at a large scale.

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

Coordination of multiple UAVs is an essential technology for various applications in robotics, automation, and artificial intelligence. In general, it includes 1) waypoints assignment and 2) trajectory generation. In this paper, we propose a new method for this problem. First, we modify the concept of the standard visibility graph to greatly improve the optimality of the generated trajectories and reduce the computational complexity. Second, we propose an efficient stochastic approach using simulated annealing that assigns waypoints to each UAV from the constructed visibility graph. Third, we describe a method to detect collision between two UAVs. FinallY, we suggest an efficient method of controlling the velocity of UAVs using A* algorithm in order to avoid inter-UAV collision. We present simulation results from various environments that verify the effectiveness of our approach.