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ÖgeDesign, simulation, and fabrication of a circularly polarized MIMO antenna with improved isolation(Graduate School, 2025-03-07)Circularly polarized (CP) antennas have become increasingly integral in modern wireless communication systems due to their unique ability to address common challenges such as multipath fading and polarization mismatch. These capabilities make them highly desirable for Wireless Local Area Networks (WLAN) applications, Industrial, Scientific, and Medical (ISM) bands, and satellite communications. One of the most notable advantages of CP antennas over their linearly polarized counterparts is their ability to eliminate the need for precise alignment between transmitting and receiving antennas. This feature significantly enhances flexibility in system design and operational convenience, particularly in scenarios involving dynamic or unpredictable antenna orientations. Integrating CP antennas with multiple-input multiple-output (MIMO) systems further elevates their utility in wireless communication. MIMO technology employs multiple antenna elements to achieve several critical performance enhancements, including increased channel capacity, diversity gain, improved data rates, and enhanced signal quality. These benefits are pivotal for meeting the ever-growing demands for high-speed, reliable communication in modern applications. However, the close proximity of multiple antenna elements in MIMO systems often leads to mutual coupling, which can degrade isolation and correlation performance, thereby limiting the overall system efficiency. To address these challenges, this study proposes a novel CP MIMO antenna design that incorporates an innovative decoupling structure. This design specifically targets the reduction of mutual coupling between adjacent antennas, thereby enhancing isolation and overall performance. The proposed antenna design demonstrates significant advancements over conventional CP MIMO antennas, making it a robust solution for high-performance wireless communication systems. The experimental evaluation of the proposed CP MIMO antenna reveals impressive performance metrics. The isolation between the antenna ports achieves a minimum value of 15.2 dB, marking a substantial improvement compared to conventional designs. This enhanced isolation directly contributes to reduced inter-port interference, which is crucial for maintaining signal integrity and achieving optimal MIMO performance. Additionally, the antenna exhibits commendable gain values of 6.28 dBic for right-hand circular polarization (RHCP) and 6.05 dBic for left-hand circular polarization (LHCP). These gain values indicate the antenna's ability to effectively radiate and receive circularly polarized signals, making it highly suitable for applications requiring robust polarization performance. The peak efficiency of the antenna exceeds 62%, highlighting its energy-efficient design. Such efficiency levels are critical for modern communication systems, where minimizing power consumption without compromising performance is a key requirement. Furthermore, the use of parasitic elements as decoupling stubs in the design plays a pivotal role in minimizing mutual coupling. These stubs act as reactive loads, altering the electromagnetic interaction between adjacent antennas. By strategically tuning the length and position of the stubs, the design achieves near-ideal impedance matching and optimized current distribution, leading to enhanced isolation and stable radiation patterns. The innovative features of the proposed CP MIMO antenna have a direct and positive impact on overall system performance. The stable radiation patterns ensure consistent coverage and signal quality across the operating frequency band, while the enhanced isolation minimizes the risk of interference and signal degradation. These attributes make the antenna particularly well-suited for environments characterized by high data traffic and stringent performance requirements, such as satellite communications, WLAN, and ISM band applications. Moreover, the dual-sense circular polarization of the antenna enables it to handle diverse signal orientations effectively. The RHCP and LHCP capabilities ensure compatibility with various transmission and reception scenarios, further enhancing the antenna's versatility. This dual-sense feature, combined with the superior isolation and gain characteristics, makes the proposed antenna an ideal choice for advanced MIMO systems that demand high reliability and performance. In conclusion, the CP MIMO antenna proposed in this work represents a significant advancement in antenna design for high-performance wireless communication systems. By incorporating an innovative decoupling structure and optimizing the use of parasitic elements, the antenna achieves remarkable isolation, stable radiation patterns, and efficient multipath handling. These features collectively ensure superior performance in challenging communication environments, where reliability and data integrity are paramount. The experimental results validate the antenna's potential for real-world applications, demonstrating its suitability for use in WLAN, ISM bands, and satellite communications. The enhanced isolation of 15.2 dB, gain values of 6.28 dBic and 6.05 dBic, and efficiency exceeding 62% position the proposed antenna as a cutting-edge solution for modern wireless systems. Its ability to address mutual coupling and polarization challenges makes it a valuable asset for next-generation MIMO systems, paving the way for reliable and high-speed communication in diverse scenarios. This work not only highlights the potential of CP MIMO antennas in advancing wireless technology but also provides a foundation for future research and development. The innovative design principles and performance optimizations presented here can serve as a benchmark for developing even more efficient and versatile antenna systems, driving further progress in the field of wireless communications.
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ÖgeThe spatio-temporal dynamics of aerosols in the Marmara region and impact of land cover/use on atmospheric environment(Graduate School, 2023-06-16)The impacts of urbanization and industrialization on air quality are well known, and air pollution control strategies have been implemented, but the effectiveness of these strategies is limited to developed countries. Developing countries continue to rely on fossil fuels and experience air pollution from agriculture emissions, crop residue burning, biomass fuel, and low-quality coal combustion. Air pollution contributes to health, visibility, ecosystems, and climate change. Solid air pollutants, also known as particulate matter (PM) consist of tiny particles suspended in the air has the most severe health impacts. Governments need to measure, monitor, and control the concentration of air pollutants and maintain the pollution levels under the values defined by the World Health Organization (WHO). There have been some efforts to reduce air pollution in developing countries. However, there is still a long way to go in terms of clean air in many developing countries. The data collected from air quality monitoring (AQM) stations are commonly used to study the impacts of air pollution on human health. However, the initial investment and maintenance of ground-based AQM stations are expensive. The limited quantity of air quality monitoring stations on the ground poses a hindrance to conducting thorough research into the effects of elevated levels of air pollution on the environment. Measurements from earth-observing satellites can be a useful substitute for ground-based data in environmental studies. Although ground-based data are often more detailed and accurate, satellite data provide a broader view of larger spatial extents. The advantages of satellite data collection over a lengthy period allow for long-term monitoring of environmental trends and changes. Aerosols, tiny particles that are suspended in the Earth's atmosphere can be of natural or human-made origin. The impact of atmospheric aerosol loading on various aspects of the Earth's climate and environment cannot be overstated. Besides its adverse effect on human health, it affects global temperature, atmospheric radiation, the Earth's albedo, and terrestrial heat budget, as well as clouds and precipitation processes, and ecological systems. Therefore, understanding atmospheric aerosols is essential for comprehending Earth's climate and ecosystem. The columnar aerosol pollution or the number of microscopic aerosol particles in a vertical slice of the atmosphere can be measured by satellite sensors. The main remotely sensed geophysical quantity and column-effective particle property is total column aerosol optical depth (AOD). The AOD is a measure of how much sunlight is absorbed or scattered by aerosol particles in the atmosphere, and it provides information on the concentration and distribution of atmospheric aerosols. The concentrations of PM and AOD are important measures of air pollution. They represent the amount of particulate matter present in the air and are strongly correlated with each other. While ground-based air pollutant concentration records show the concentration of pollutants near the Earth`s surface, satellite-based and ground-measured AOD data provide a measure of the total column amount of aerosols from the Earth's surface up to the top of the atmosphere. Satellite-based AOD measurements can identify hotspots of particle pollution and short-term spikes, which can then be targeted for more detailed ground-based measurements of air pollution. Space-based remote sensing plays a vital role in characterizing the spatial and temporal distributions of atmospheric aerosols from the local to global scale. The popularity of satellite aerosol products has led to the development of various aerosol retrieval algorithms. To ensure the accuracy of satellite aerosol data in interpreting regional and global aerosol patterns, it's crucial to evaluate the performance of the retrieval algorithms. Validation using accurate ground-based AOD measurements is employed for this purpose. AErosol RObotic NETwork (AERONET) program is a federation of ground-based remote sensing aerosol networks, a vital worldwide network for monitoring aerosols that employ numerous sun-photometers stationed across the globe to measure various aerosol optical and microphysical characteristics, such as AOD. It's important to note that the efficacy of satellite aerosol products may vary depending on factors such as geography, climate, and weather conditions. Thus, it's essential to identify the most reliable algorithm by validating satellite AOD retrievals against the nearest ground-based data to the region of interest. The primary aim of this research is to better understand the behavior of aerosols in the atmosphere and gain insight into the spatial distribution and temporal variability of aerosols in the region, as well as to identify the factors that influence the magnitude of AOD and its correlation with land cover/use (LCU). This understanding will aid in devising an efficient strategy to manage environmental air pollution. Furthermore, by gathering, verifying, and analyzing appropriate data through particular techniques, the alterations in aerosol concentrations over a prolonged period will be assessed. For this major objective, the study is divided into three key sections. First, various aerosol products are evaluated for their accuracy and reliability by comparing satellite-derived AODs with AERONET AOD measurements at three different sites. The most effective AOD product were determined by comparing the performances of aerosol data sets. In this context, multiple Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD products were compared with AERONET AOD measurements at various surface classes/types (i.e. land, ocean, coastal) in the eastern Mediterranean over five years (2014-2018) and the results were validated. MODIS aerosol products based on Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) are available at different spatial resolutions (1 km, 3 km, and 10 km), while the VIIRS aerosol product has a resolution of 6 km. To obtain VIIRS AOD values, the improved DB approach was used for land and the Satellite Ocean Aerosol Retrieval (SOAR) method for the ocean. The best-performing products over the urban/land surface are the MODIS MAIAC and VIIRS DB aerosol products with Root-Mean-Squared Errors (RMSE) of 0.048-0.061 and a higher percentage (81%-89%) of retrievals falling within the expected error for land. This is while the MODIS MAIAC AOD data also has the advantage that it has a better spatial resolution of 1 km, which enables it to capture sub-grid aerosol features and a higher number of AOD retrievals. In the second section, I focus on the spatiotemporal variation of MODIS MAIAC AOD over the Marmara region. The objective of this section is to pinpoint areas within a region that have high AOD levels and examine how various environmental factors such as seasonal weather patterns, local emission sources, and LCU are linked to the presence of aerosol pollution in those areas. Our study area was the Marmara region because it is subject to the production of aerosols originating from diverse sources, both natural and anthropogenic. The Marmara Region is the country's most populous region, despite being the second smallest geographically. This is due to Istanbul being located there. The region also contains other important developing cities like Bursa, Kocaeli, and Tekirdag. It is a significant economic center with a lot of agricultural, commercial, and industrial activity. Aerosol formation in the region is influenced by a range of sources, including sea salt, agricultural practices, maritime transport, industrial activities, and the seasonal transportation of dust from the Sahara Desert. To accomplish the objective of the study's second part, I analyzed the MODIS MAIAC AOD data at annual, monthly, and seasonal scales between 2000 and 2021 to investigate the spatiotemporal variability of AOD in the Marmara region. The monthly mean AOD increases gradually from January to May and fluctuates between May and August and reaches its highest value in August. The monthly mean AOD decreases after August and reaches its lowest monthly mean at the end of the year during December. Seasonal variation in AOD is significant: summer (0.148) > spring (0.136) > autumn (0.116) > winter (0.09). According to an analysis of the AOD's multi-year variation, there were two maxima for the AOD between 2000 and 2010 with values of 0.146 and 0.137 in 2002 and 2008, respectively. AOD exhibits a decreasing tendency from 2000 to 2021, with a 0.005/yearly decline. Using the MODIS MAIAC AOD data at a 1-km scale, I performed a comprehensive assessment of aerosol loading and statistical-visual analyses highlighting the influence of land use/cover on aerosol properties. The findings revealed that there are significant regions with high aerosol concentrations across the region and that these regions show significant temporal variations. The aerosol loading was higher over the western side of the Marmara region (Edirne, Tekirdag, Kırklareli, Canakkale, Istanbul, Kocaeli) until 2011 while the eastern part of the region (Bursa, Sakarya, and Balikesir) was exposed to higher aerosol concentrations after 2016. From 2000 to 2021 the largest number of days with lower aerosol pollution is seen in Bilecik (≈ 35%) while Edirne experienced the highest percentage of days with the highest aerosol pollution level (≈ 9%). In the region, aerosols are mostly generated by urban activities and industries, as well as mineral aerosols originating from soils. Finally, a novel approach to LCU classification is proposed. The last part of the paper proposes a strategy to identify and distinguish different LCU patterns in Mediterranean cities. The focus was on separating built-up areas from bare land, which can be challenging due to urban landscape complexity and heterogeneity. Separation is also necessary since urbanized/industrialized zones and bare soil areas contribute significantly to atmospheric aerosol pollution. For this purpose, the separation of these two classes was well-addressed by using the proposed multi-index methods on satellite image data. The multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) showed outstanding overall performance with 93% accuracy and a 0.91 kappa value for all LCU classes. The improvement achieved in separating built-up regions from bare land is of substantial significance, as it significantly reduces the misclassification of bare land as built-up regions. This is particularly important for aerosol studies in the study region, where the two factors with the highest impact on aerosol loading are found. The enhanced accuracy of land cover classification provided by this improvement can greatly enhance the reliability and precision of aerosol studies in the area.
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ÖgeA deep learning based framework for identification of ship types using optical satellite images(Graduate School, 2023-01-18)Today, monitoring of ship traffic in open and coastal seas is one of the primary and even indispensable activities of coastal countries for many reasons. Preventing activities such as illegal fishing, migration, smuggling, pollution of the seas, protection of underground resources and sea creatures, defensive reconnaissance, and surveillance activities can be listed as the main reasons for monitoring ship activity in the seas. Periodic monitoring of open and coastal seas using Earth observation satellites is a significant approach, providing fast and cost-effective results as well as large coverage extents. With this perspective, the thesis presents research about an effective and end to end ship monitoring approach via optical satellites. The use of deep learning (DL) techniques for the detection and classification of ships using optical satellite images, the creation of a ship database consisting of optical satellite images for the verification of the subject, and optical constellation modeling are the topics of this research. In the thesis, a comprehensive literature review is presented with the scope of gabs in the ship monitoring with using optical satellites. To mimic the real satellite image input, an optical satellite based image dataset – VHRShips - was formed. VHRShips consists of a total of 6312 images, 1000 without ships and 5312 with ships. There are a total of 11337 ships in the images. The database created includes 24 different types, and navy ships, one of these types, contain 11 different types. This dataset stands out with its rich ship database content and large sample size. Thesis reinterprets DL-based target detection and classification algorithms and proposes a flexible target detection and classification approach in a hierarchical design (HieD) that allows easy addition and removal of different algorithms. In addition, the detection and localization, recognition, and identification (DLRI) steps are staged and the outputs of the algorithm are detailed. In the phase of detecting, the presence of ships is verified in the images provided by the satellites. The determination of the positions of the ships in the images is carried out during the localization stage. The classification of the ships among the determined basic ship types is defined in the recognition stage. Lastly, the classification of navy ships, which is one of the main ship types, is handled during the identification stage. The feasibility of the developed approach has been demonstrated by the preliminary feasibility analysis for the covering of the Turkish surrounding seas with optical constellations. In this analysis, an optimization was carried out by using a software on how the satellite images required for the proposed method can be realized with a set of satellite design. It has been determined that 40 optical satellites are required to fully cover the selected sea area in 24 hours, and 100 optical satellites with the specified characteristics to be covered twice. The results of proposed method, HieD are presented in three formats which are; the individual stage performances, a comprehensive end-to-end evaluation and lastly a comparison with a well-known method, YOLOV4. The results of the thesis are very promising. F1 scores for detection, recognition, localization, and identification were respectively 99.17%, 94.20%, 84.08%, and 82.13% as a consequence of stage-by-stage optimization. The F1 scores for the same order after complete implementation of our suggested method were 99.17%, 93.43%, 74.00%, and 57.05%. End-to-end YOLOv4 produced F1-scores for DLRI of 99.17%, 86.59%, 68.87%, and 56.28%, in opposition. For the steps of localization, recognition, and identification, we outperformed YOLOv4 using HieD. In the thesis; it has been shown that the ship detection, localization, recognition and identification method developed in a hierarchical structure using a challenging data set containing images with different backgrounds in open and coastal seas in many different geographies, works successfully. The method is very open to development with the antecedent and intermediate methods to be added. The data set created in the thesis has been used with various detection and classification techniques, proving that there is no dataset that provides a limited opportunity for the study. VHRShips can serve as a standard for the developed approach as well as for further research into the use of deep learning and advancing geospatial AI applications in the maritime environment. Finally, the thesis presents a vision of the future along with a fair self-criticism.
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ÖgeFiber optic network-based remote sensing of rail systemsvehicles(Graduate School, 2024-02-16)In recent years, the need for precise positioning of moving vehicles has become increasingly important, especially in smart cities. Accurate positioning is critical for an efficient and safe transportation system. However, the existing positioning systems are often limited because they are point-based, and the error margin for non-point-based systems is usually more than one meter, sometimes even exceeding ten meters. This limitation significantly reduces the potential use of these positioning technologies, particularly in urban metro systems. To address the accuracy problem in fast positioning, a new hybrid system utilizing Fiber Bragg Gratings (FBG) technology is proposed within the scope of this thesis. One unique feature of this designed model is the ability to encode the transmitted signal. Another essential feature is the correlation between the reference and reflected signals. This allows for precise positioning, even at high noise levels. With the help of the coding of transmitter signal 1, 2, 4, 8, 16, 32, 64, 128, 256-bit pulse sequences have been created for the transmission side, and the localization errors were 118.4, 68.1, 4.8, 0.4, 0.3, 0.3, 0.2, and 0.1 m respectively for 50 km track line under high Gaussian noise. Additionally, the hybrid system can determine the speed and direction of a moving object by interpreting two consecutive or just a single transmitted signal. Speed and direction estimation can also be achieved by examining the frequency shift of the signal. This approach contributes to speed, position, and direction determination without analyzing multiple signals, providing higher accuracy and protection against erroneous data. The research aims to address four main questions: 1. Which should be chosen between FBG and Distributed Acoustic Sensing (DAS) methodologies for sensor applications? 2. What technical specifications are essential for implementing the hybrid FBG and DAS system to position mobile vehicles in railway applications precisely? 3. How do the hybrid system's performance, speed, and direction determination capabilities compare to existing systems, and what are the optimal configurations and parameters of the proposed system? 4. What are the hybrid system's potential uses, advantages, and limitations in various transportation types, and how can it enhance the efficiency and safety of urban transportation systems? The literature review has indicated the positioning capabilities, technical specifications, and limitations of FBG and DAS systems. Using fiber optic sensors, these systems expose external stimuli to measure stress and temperature changes in optical fibers. These changes cause a shift in the Bragg wavelength of FBG, which can be detected by measuring the reflected signal from the fiber. Furthermore, DAS, another sensing system, is used to detect the position and movement of an object by utilizing acoustic waves originating from external stimuli. However, the random nature of the backscattered signal limits the mathematical modeling to randomness. Development studies are ongoing in this scope. An advanced simulation software, using Optiwave's OptiSystem, was conducted for a newly developed and tested design to evaluate the hybrid system's performance. The software can simulate FBG sensors based on a mathematical model with a limited randomness of the reflected and transmitted signals. The effect of train on FBG expressed related to IEEE 1698 std. which defines air resistance force at the surface of the train which is directly effects the FBG sensor. This model was developed and verified with field tests to provide the highest accuracy and reliability during simulation. The behavior of FBG sensors calibrated and optimized related to field results. Also, the proper FBG working frequencies selected related to the FS Community channels guide. The performance of the proposed hybrid system was carefully evaluated and compared to existing systems. The most suitable configurations and parameters for the designed model were determined. The design and implementation of the system were evaluated considering the provided accuracy, speed, direction determination, and potential applications in various transportation contexts. To showcase the system's capabilities, a cleaning train cleaning operation in clearing the track was modeled and demonstrated using the hybrid system. Various measures against foreign objects on the track could also be provided. The proposed hybrid sensing system fills a gap in the literature by analyzing potential applications in the railway transportation sector. It could contribute significantly to the knowledge of high-precision positioning for detecting moving vehicles. This thesis extensively researches the implementation of a hybrid system for high-precision positioning in urban transportation. Addressing the research questions above demonstrates this model's feasibility and potential to revolutionize the urban transportation sector. The published articles from this thesis have significantly contributed to the knowledge of high-precision positioning systems and their applications in the transportation sector. The proposed hybrid system has several potential uses, advantages, and limitations in various transportation types. For instance, the system can be used in railway transportation to track trains and ensure their safe and efficient operation. The system can also be used in road transportation to track vehicles and optimize traffic flow. Additionally, the system can be used in air transportation to track airplanes and ensure their safe and efficient operation. One of the significant advantages of the hybrid system is its high accuracy in positioning, speed, and direction determination. The system can provide accurate positioning even at high noise levels, making it suitable for use in busy urban environments. The system is also robust and can withstand harsh environmental conditions, making it suitable for use in various transportation contexts. However, the hybrid system has certain limitations that need to be considered. For instance, the system requires a network of sensors to provide accurate positioning, which can be costly to implement. In conclusion, the proposed hybrid system utilizing Fiber Bragg Gratings (FBG) technology is a promising solution to the accuracy problem in real-time positioning. The system can provide high-precision positioning, speed, and direction determination, making it suitable for use in various transportation contexts. The system has several potential uses, advantages, and limitations that need to be considered when implementing it. Overall, the system has the potential to revolutionize the urban transportation sector by ensuring safe and efficient operation.
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ÖgeSingle-frame and multi-frame super-resolution on remote sensing images via deep learning approaches(Graduate School, 2022-07-29)As a quite significant computer vision task, image super-resolution (SR) has been widely applied in remote sensing (RS), medical imaging, video surveillance, and biometrics. Image SR aims to restore high-resolution (HR) images by enhancing the spatial, spectral, or temporal resolution of the low-resolution (LR) inputs. In recent years, great efforts have been made for improving the SR approaches. One of the approaches is to design deeper networks. Nevertheless, this greatly increases computation and memory consumption. As a result, some mechanisms (such as cascading networks, attention mechanisms, and back projection) are proposed to improve the performance and the training process of the complex networks. Satellite imagery can be seen in various fields, namely Land cover/Land use classification, road and building extraction, observation of climate, and earthquake prediction. However, in some cases, the resolution of satellite images can not meet the application requirements due to the technology and cost limitations during the satellite design; therefore, the improvement of image resolution might be necessary. Since it is not possible to upgrade the equipment onboard the launched satellite, the software-based SR algorithms are deserved to be explored in RS fields. This thesis aims to strengthen the spatial resolution of optical satellite imageries by using deep learning (DL) methods. Generally, image SR algorithms can be categorized as single-frame image SR (SFSR) and multi-frame image SR (MFSR). The inputs of the SFSR can be a single LR image. While multi-frame image SR aims to restore HR image by using multiple LR images, which can be obtained under different conditions and at different angles. Recently, great contributions have been made to improve the SR methods including two aspects: (1) increasing the value of PSNR (Peak-Signal-Noise-Ratio); (2) improving the image quality perceptually. Nevertheless, some algorithms obtain a high PSNR but with a low perceptual quality which is more important to human perception. Therefore, this thesis has the following objectives: (1) Explore a perceptual-driven approach to enhance the SR image quality visually on single-frame and multi-frame RS imageries; (2) Explore Generative Adversarial Network (GAN)-based models for single-frame and multi-frame RS imagery super-resolution task to fulfill the multi-scale problem and blind to the degradation model; (3) Explore an image fusion method that can generate an arbitrary size of the super-resolved image rather than a small patch. This thesis firstly gives an overview of single-frame and multi-frame RS image SR methods. The single-frame RS image SR methods are briefly classified into supervised and unsupervised methods. The former mainly includes Convolutional Neural Networks (CNN)-based, GAN-based, attention-based, and Back-projection based methods. In addition, the commonly used attention mechanisms including self-attention, channel attention, spatial attention, mixed high-order attention (MHOA), non-local attention (NLA), and non-local sparse attention (NLSA) are also introduced. Moreover, loss functions including pixel-wise loss, perceptual loss, adversarial loss, and cycle consistency loss are presented. For the single-frame RSISR, firstly, an attention CNN-based SR method is proposed. Although CNN-based algorithms have made outstanding achievements in computer vision tasks, the traditional CNNs methods treat the abundant low-frequency information included in the LR inputs equally across channels. Attention-guided algorithms play a vital role in the informative features extraction in various tasks including image SR. With the application of the attention mechanism, the proposed CNN-based method can further learn the deeper relationships among the different channels. Instead of simply integrating the attention module with the residual blocks, a Layer Attention Module (LAM) and Spatial Attention Module (SAM) are proposed to further learn the relationships among the Residual Groups (RG). Moreover, the perceptual loss function is adopted in the training process to enhance the generated image quality perceptually, and Random down-sampling is applied to strengthen the model's generalization ability. Secondly, an attention GAN-based super-resolution method is explored for the single-frame RS images. CNN-based methods have made great contributions to increasing the value of PSNR/SSIM. Nevertheless, the generated outputs tend to be overly smooth and blurry. GAN can generate more realistic images than normal CNN-based methods and has been introduced to single image super-resolution (SRGAN, ESRGAN, EEGAN). Standard GANs only function on spatially local points in LR feature maps. The attention mechanism can directly learn the long-range dependencies in the feature maps both in the generator and discriminator in a powerful way. By applying the attention mechanism, the network allocates attention based on the similarity of color and texture. Therefore, based on ESRGAN, an attention GAN-based method is for the single-frame RS image SR. The ESRGAN was mainly improved from two aspects: (1) we further improved the architecture of the residual blocks by adding more skip connections; (2) we add attention modules to the residual blocks for further feature extraction. Moreover, instead of working on aerial photographs or low-resolution and medium-resolution satellite images, we are focusing on the Very High-Resolution (VHR) satellite imageries, such as the Pleiades, and Worldview-3. The spatial resolutions of the multispectral images for the Pleiades, and Worldview-3 are 2m, and 1.24m, respectively. Furthermore, for the attention CNN-based method, we evaluated the method on the Pleiades and Worldview-3 datasets with scaling factors of 2, 4, and 8. For the attention GAN-based method, we evaluated the method on the Pleiades and Worldview-3 datasets with a scaling factor of 4. The experimental results show the attention-based method can provide better perceptual results both quantitatively and qualitatively. At last, we proposed an attention GAN-based method for the multi-frame RS image SR. Firstly, we introduced an attention mechanism to the Generator and proposed a space-based network that worked on every single frame for better temporal information extraction. Secondly, we proposed a novel attention module for better spatial and spectral information extraction. Thirdly, we applied an attention-based discriminator to enhance the discriminator's discriminative ability. Finally, the experimental results on the SpaceNet7 dataset and Jilin-1 dataset exhibit the superior of the proposed model both quantitatively and qualitatively.