Image Simulation in Remote Sensing

Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers

Efficient architectures of heterogeneous fpga-gpu for 3-d medical image compression

The advent of development in three-dimensional (3-D) imaging modalities have generated a massive amount of volumetric data in 3-D images such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US). Existing survey reveals the presence of a huge gap for further research in exploiting reconfigurable computing for 3-D medical image compression. This research proposes an FPGA based co-processing solution to accelerate the mentioned medical imaging system. The HWT block implemented on the sbRIO-9632 FPGA board is Spartan 3 (XC3S2000) chip prototyping board. Analysis and performance evaluation of the 3-D images were been conducted. Furthermore, a novel architecture of context-based adaptive binary arithmetic coder (CABAC) is the advanced entropy coding tool employed by main and higher profiles of H.264/AVC. This research focuses on GPU implementation of CABAC and comparative study of discrete wavelet transform (DWT) and without DWT for 3-D medical image compression systems. Implementation results on MRI and CT images, showing GPU significantly outperforming single-threaded CPU implementation. Overall, CT and MRI modalities with DWT outperform in term of compression ratio, peak signal to noise ratio (PSNR) and latency compared with images without DWT process. For heterogeneous computing, MRI images with various sizes and format, such as JPEG and DICOM was implemented. Evaluation results are shown for each memory iteration, transfer sizes from GPU to CPU consuming more bandwidth or throughput. For size 786, 486 bytes JPEG format, both directions consumed bandwidth tend to balance. Bandwidth is relative to the transfer size, the larger sizing will take more latency and throughput. Next, OpenCL implementation for concurrent task via dedicated FPGA. Finding from implementation reveals, OpenCL on batch procession mode with AOC techniques offers substantial results where the amount of logic, area, register and memory increased proportionally to the number of batch. It is because of the kernel will copy the kernel block refer to batch number. Therefore memory bank increased periodically related to kernel block. It was found through comparative study that the tree balance and unroll loop architecture provides better achievement, in term of local memory, latency and throughput

Deep learning architectures for 2D and 3D scene perception

Scene understanding is a fundamental problem in computer vision tasks, that is being more intensively explored in recent years with the development of deep learning. In this dissertation, we proposed deep learning structures to address challenges in 2D and 3D scene perception. We developed several novel architectures for 3D point cloud understanding at city-scale point by effectively capturing both long-range and short-range information to handle the challenging problem of large variations in object size for city-scale point cloud segmentation. GLSNet++ is a two-branch network for multiscale point cloud segmentation that models this complex problem using both global and local processing streams to capture different levels of contextual and structural 3D point cloud information. We developed PointGrad, a new graph convolution gradient operator for capturing structural relationships, that encoded point-based directional gradients into a high-dimensional multiscale tensor space. Using the Point- Grad operator with graph convolution on scattered irregular point sets captures the salient structural information in the point cloud across spatial and feature scale space, enabling efficient learning. We integrated PointGrad with several deep network architectures for large-scale 3D point cloud semantic segmentation, including indoor scene and object part segmentation. In many real application areas including remote sensing and aerial imaging, the class imbalance is common and sufficient data for rare classes is hard to acquire or has high-cost associated with expert labeling. We developed MDXNet for few-shot and zero-shot learning, which emulates the human visual system by leveraging multi-domain knowledge from general visual primitives with transfer learning for more specialized learning tasks in various application domains. We extended deep learning methods in various domains, including the material domain for predicting carbon nanotube forest attributes and mechanical properties, biomedical domain for cell segmentation.Includes bibliographical references

Heterogeneous Networking for Beyond 3G system in a High-Speed Train Environment. Investigation of handover procedures in a high-speed train environment and adoption of a pattern classification neural-networks approach for handover management

Based on the targets outlined by the EU Horizon 2020 (H2020) framework, it is expected that heterogeneous networking will play a crucial role in delivering seamless end-to-end ubiquitous Internet access for users. In due course, the current GSM-Railway (GSM-R) will be deemed unsustainable, as the demand for packet-oriented services continues to increase. Therefore, the opportunity to identify a plausible replacement system conducted in this research study is timely and appropriate. In this research study, a hybrid satellite and terrestrial network for enabling ubiquitous Internet access in a high-speed train environment is investigated. The study focuses on the mobility management aspect of the system, primarily related to the handover management. A proposed handover strategy, employing the RACE II MONET and ITU-T Q.65 design methodology, will be addressed. This includes identifying the functional model (FM) which is then mapped to the functional architecture (FUA), based on the Q.1711 IMT-2000 FM. In addition, the signalling protocols, information flows and message format based on the adopted design methodology will also be specified. The approach is then simulated in OPNET and the findings are then presented and discussed. The opportunity of exploring the prospect of employing neural networks (NN) for handover is also undertaken. This study focuses specifically on the use of pattern classification neural networks to aid in the handover process, which is then simulated in MATLAB. The simulation outcomes demonstrated the effectiveness and appropriateness of the NN algorithm and the competence of the algorithm in facilitating the handover process

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Advanced Pre-Processing and Change-Detection Techniques for the Analysis of Multitemporal VHR Remote Sensing Images

Remote sensing images regularly acquired by satellite over the same geographical areas (multitemporal images) provide very important information on the land cover dynamic. In the last years the ever increasing availability of multitemporal very high geometrical resolution (VHR) remote sensing images (which have sub-metric resolution) resulted in new potentially relevant applications related to environmental monitoring and land cover control and management. The most of these applications are associated with the analysis of dynamic phenomena (both anthropic and non anthropic) that occur at different scales and result in changes on the Earth surface. In this context, in order to adequately exploit the huge amount of data acquired by remote sensing satellites, it is mandatory to develop unsupervised and automatic techniques for an efficient and effective analysis of such kind of multitemporal data. In the literature several techniques have been developed for the automatic analysis of multitemporal medium/high resolution data. However these techniques do not result effective when dealing with VHR images. The main reasons consist in their inability both to exploit the high geometrical detail content of VHR data and to model the multiscale nature of the scene (and therefore of possible changes). In this framework it is important to develop unsupervised change-detection(CD) methods able to automatically manage the large amount of information of VHR data, without the need of any prior information on the area under investigation. Even if these methods usually identify only the presence/absence of changes without giving information about the kind of change occurred, they are considered the most interesting from an operational perspective, as in the most of the applications no multitemporal ground truth information is available. Considering the above mentioned limitations, in this thesis we study the main problems related to multitemporal VHR images with particular attention to registration noise (i.e. the noise related to a non-perfect alignment of the multitemporal images under investigation). Then, on the basis of the results of the conducted analysis, we develop robust unsupervised and automatic change-detection methods. In particular, the following specific issues are addressed in this work: 1. Analysis of the effects of registration noise in multitemporal VHR images and definition of a method for the estimation of the distribution of such kind of noise useful for defining: a. Change-detection techniques robust to registration noise (RN); the proposed techniques are able to significantly reduce the false alarm rate due to RN that is raised by the standard CD techniques when dealing with VHR images. b. Effective registration methods; the proposed strategies are based on a multiscale analysis of the scene which allows one to extract accurate control points for the registration of VHR images. 2. Detection and discrimination of multiple changes in multitemporal images; this techniques allow one to overcome the limitation of the existing unsupervised techniques, as they are able to identify and separate different kinds of change without any prior information on the study areas. 3. Pre-processing techniques for optimizing change detection on VHR images; in particular, in this context we evaluate the impact of: a. Image transformation techniques on the results of the CD process; b. Different strategies of image pansharpening applied to the original multitemporal images on the results of the CD process. For each of the above mentioned topic an analysis of the state of the art is carried out, the limitations of existing methods are pointed out and the proposed solutions to the addressed problems are described in details. Finally, experimental results conducted on both simulated and real data are reported in order to show and confirm the validity of all the proposed methods

Illumination and Noise-Based Scene Classification - Application to SAR Sea Ice Imagery

Spatial intensity variation introduced by illumination changes is a challenging problem for image segmentation and classification. Many techniques have been proposed which focus on removing this illumination variation by estimating or modelling it. There is limited research on developing an illumination invariant classification technique which does not use any preprocessing. A major focus of this research is on automatically classifying synthetic aperture radar (SAR) images. These are large satellite images which pose many challenges for image classification including the incidence angle effect which is a strong illumination variation across the image. Mapping of full scene satellite images of sea-ice is important for navigational purposes for ships and also for climate research. The images obtained from the RADARSAT-2 satellite are dual band, high quality images. Currently, sea ice chart are produced manually by ice analysts at the Canadian Ice Service. However, this process can be automated to reduce processing time and obtain more detailed pixel-level ice maps. An automated classification algorithm to achieve sea ice and open water separation will greatly help the ice analyst by providing sufficient guidance in the initial stages of creating an ice map. It would also help the analyst to improve the accuracy while finding ice concentrations and remove subjective bias. The existing Iterative Region Growing by Semantics (IRGS) algorithm is not effective for full scene segmentation because of the incidence angle effect. This research proposes a "glocal" (global as well as local) approach to solve this problem. The image is divided in a rectangular grid and each rectangle is segmented using IRGS. This is viewed as an over-segmentation of the original image. Finally, IRGS is used globally to glue together the over-segmented regions. This method yields acceptable results with the denoised images. The proposed technique can also be used for general image classification purposes. Extensive testing was done to investigate the best set of parameters for the proposed approach. Images were simulated with the SAR illumination variation and multiplicative speckle noise. The technique was effective for general classification and attained accurate results for full scene SAR segmentation