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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.
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ÖgeDistrict-based urban sprawl monitoring and modelling using CA-Markov model: application in two mega cities(Graduate School, 2022-12-12)There is a well-known fact that the population all around the world is growing so fast and current estimation and trends forecast a continuous rise in world population in the future. Urbanization occurs due to the high rate of immigration from rural areas to urban areas resulting in urban growth and forcing urban development toward the border of the city. The unstructured growth of urban areas can lead to urban sprawl. There are various methods and data to observe and quantify the changes induced through urban expansion. Remote sensing and Geographical Information Systems (GIS) can be named as one of the most helpful tools in the detection of the spatio-temporal dynamics of patterns of urban growth and Land Use/Land Cover (LULC) changes at local, regional, and global scales. The main aims and objectives of this study are to evaluate the spatio-temporal pattern of urban growth over time, to predict future urban expansion, and draw attention to district- based urban expansion. Thus, in this research, the most urbanized districts of Istanbul and Sydney, were selected to study. Identification of potential areas for future urban development is important in the selection of study areas. Arnavutköy district of Istanbul has a high potential of urban development due to the construction of the Istanbul airport. On the other hand, Hills Shire Local Government Area (LGA) was selected due to the improvement in the transportation system which can affect future urban development. This study consists of two main parts: urban expansion analysis and urban expansion modelling. As a first step in urban expansion analysis multitemporal Landsat satellite images of Arnavutköy district (18-July-1997, 31-August-2007, and 25-July-2017) and Hills Shire LGA (08-December-1996, 05-January-2007, and 19-January-2018) were acquired. After pre-processing of obtained satellite images of both study areas, images were classified into five LULC classes using Maximum Likelihood supervised classification algorithm. Accuracy assessment was also done to evaluate the accuracy of LULC maps of each study area. The overall accuracy values of Arnavutköy district in 1997, 2007, and 2017 were found as 91%, 86%, and 88%, respectively. Likewise, in Hills Shire LGA, the overall accuracy values were acquired as 92%, 95%, and 91% in 1996, 2007, and 2018, respectively. The post-classification change detection technique was employed to identify conversions from one LULC class to another LULC class. The results of change detection analysis showed that the built-up area in Arnavutköy district grew up to 197% while Hills Shire LGA expanded by 78% through the study period. In both study areas, in opposition to the built-up area, forest, and agricultural area experienced a downward trend and lost a lot of hectares during the period of study. To analyze the impact of urban expansion on the Land Surface Temperature (LST) of the surrounding environment, the correlation analysis between satellite retrieved LST and the built-up area in the selected buffer zones were carried out. The results revealed that, with the increase in built-up areas, the LST has also increased over the study period in Arnavutköy district and Hills Shire LGA. This indicates that urban expansion can cause to increase in the temperature of the urban areas which may also have negative effects on the climate changes. The degree of urban sprawl and the spatial pattern of urban expansion were measured using Shannon's entropy approach for each study area. The results indicated that, in both cases, urban areas had undergone a considerable urban sprawl. Furthermore, it was observed that built-up areas mostly expanded toward the North direction in both study areas, which shows the role of human-induced activities in urban expansion. As a next step, selected landscape metrics at the class level were used to better understand the urban structure. Accordingly, six metrics namely, Class Area (CA), Number of Patches (NP), Edge Density (ED), Largest Patch Index (LPI), Patch Density (PD), and Area Weighted Mean Patch Fractal Dimension (AWMPFD/FRAC-AM) were selected to describe the pattern of urban growth. The CA metric, which describes what portion of the landscape is composed of a specific patch type, was used to calculate the absolute size of the built-up area. The NP metric is a simple measure of the discontinuous built-up patch in the landscape and used as a indication on the fragmentation degree of the urban area. The PD metric is a fragmentation metric that reveals the number of patches per unit area. The absolute size of urban patches was determined using the LPI metric, which measures the percentage of entire landscape area covered by the largest patch. The ED and FRAC_AM metrics which measure the patch shape complexity were used to analyze the the shape irregularity of urban areas and spatial heterogeneity of the landscape. Metrics results which provide useful information about the aggregation and dispersion of urban also confirmed an expansion in Arnavutköy district and Hills Shire LGA. The results also showed that Arnavutköy district is more fragmented and Hills Shire LGA has a more compact urban expansion process. These results also confirm the findings of the change detecion and Shannon's entropy analysis. In the final step of this research, the second part of this study which was urban expansion modelling was carried out. Several modelling methods such as; Cellular Automata (CA), Markov Chain (MC), Logistic Regression and Artificial Neural Network have been used by many resarchers to evaluate the current and possible future state of the urban areas. The Cellular Automata model is a computational method that can simulate the process of growth by using a set of simple rules in analyzig complex systems. This model is comprised of a set of rules that define the interaction of cells and collection of variables that lead to the investigation of various urban forms. The acpability of CA to simulate urban expansion, land use change, and population growth has become appropriate for simulating complex spatial systems. The Markov Chain model is a stochastic process that experiences transitions from one state to another considering certain probabilistic rules and predicts future changes based on the past. Logistic Regression creates a model that can best describe the relationship between dependent and independent variables using the least number of variables. The Artificial Neural Networks model is a powerful tool that models patterns using a machine learning approach. ANN output values vary from 0 to 1. In urban modelling, a value of 1 represents the greatest potential for future urban growth, while a value of 0 represents the least potential for future urban growth. Each of these methods has its own advantages and disadvantages, however, the integrated models have proved to be more accurate. The integrated CA–Markov model does multiple principle evaluation functions which combines Cellular Automata and Markov Chain models together. The MC is a widely known model, which predicts future change by considering the past, however it does not take into account the spatial distribution of the categories, while CA is a dynamic model which detects the spatial location of changes. To this end, the integrated CA-Markov model was used for predicting future urban expansion. In the modelling, first, the suitability map of each LULC class were created using Multi Criteria Evaluation (MCE) method. Second, transition area and transition probability matrices were determined by using the Markov Chain model. After that, the LULC map of Arnavutköy district in 2017 and LULC map of Hills Shire LGA in 2018 were simulated using CA_Markov model. Subsequently, the model was validated by comparing the actual and simulated LULC maps of Arnavutköy district and Hills Shire LGA. After obtaining the reasonable results of validation which were above 80% for Arnavutköy district and Hills Shire LGA, the suitability maps, transition area and transition probability matrices were created to predict the future LULC maps of 2050 for both study areas. To better evaluate the urban expansion of both study areas in the future, the area statistics of the LULC map of 2050 were calculated and the results revealed that urban growth will continue in the future by increasing 45% and 51% in Arnavutköy district and Hills Shire LGA, respectively. On the other hand, the forest area will decrease in Arnavutköy district by 26%, while in Hills Shire LGA 30% of the forest will be lost throughout the 32 years. This study presents a significant contribution to land use modelling, which logically integrates remote sensing data and ancillary data to be used as an input in CA- Markov model to successfully simulate and predict the temporal and spatial changes of LULC classes. Moreover, it has been determined that using different spatial pattern analysis such as Shannon's entropy and landscape metrics can help to better evaluate the urban expansion and model the future urban growth. The findings of this study can help to better evaluate the dynamic nature of built-up areas in both Arnavutköy district and Hills Shire LGA. The simulated future LULC maps can be used as a system of early warning to recognize the consequences of land use changes. The simulation results can also be investigated as a strategic guide for urban planners to better evaluate a complex system and create optimized land use managemnet that can better control urban expansion and environmental protection. In conclusion, this study highlights the importance of district-based urban expansion analysis. However, there are many research studies on urban expansion in big cities all over the world, but there are relatively few studies that consider district-based urban expansion in megacities. Most researchers mainly focus on the physical and socio-economic issues of districts neglecting the role of urbanization in the district. In this regard, considering this issue is very important, as district-based analysis of the causes and effects of urban sprawl can provide urban planners with more detailed insights to analyze and understand the nature of this phenomenon on a city basis.