Study of Equatorial Plasma Bubbles Using ASI and GPS Systems

The plasma irregularities have been frequently observed in the F-region, at low latitude regions, due to the instability processes occurring in the ionosphere. The depletions in electron density, as compared to the background density, is a signature of the plasma irregularities. These irregularities are also known as the “equatorial plasma bubble” (EPB). These EPBs can measure by the total electron content (TEC) using GPS receiver and by images of the nightglow OI 630.0 nm emissions using all sky imager (ASI). The current chapter is based on the review on the signature of the EPBs in TEC and ASI. measurements. We have also discussed the importance of the study of EPBs.

InSAR Modeling of Geophysics Measurements

In the present work, the geometry and basic parameters of interferometric synthetic aperture radar (InSAR) geophysics system are addressed. Equations of pixel height and displacement evaluation are derived. Synthetic aperture radar (SAR) signal model based on linear frequency modulation (LFM) waveform and image reconstruction procedure are suggested. The concept of pseudo InSAR measurements, interferogram, and differential interferogram generation is considered. Interferogram and differential interferogram are generated based on a surface model and InSAR measurements. Results of numerical experiments are provided.

On the Use of Low-Cost RGB-D Sensors for Autonomous Pothole Detection with Spatial Fuzzy c-Means Segmentation

The automated detection of pavement distress from remote sensing imagery is a promising but challenging task due to the complex structure of pavement surfaces, in addition to the intensity of non-uniformity, and the presence of artifacts and noise. Even though imaging and sensing systems such as high-resolution RGB cameras, stereovision imaging, LiDAR and terrestrial laser scanning can now be combined to collect pavement condition data, the data obtained by these sensors are expensive and require specially equipped vehicles and processing. This hinders the utilization of the potential efficiency and effectiveness of such sensor systems. This chapter presents the potentials of the use of the Kinect v2.0 RGB-D sensor, as a low-cost approach for the efficient and accurate pothole detection on asphalt pavements. By using spatial fuzzy c-means (SFCM) clustering, so as to incorporate the pothole neighborhood spatial information into the membership function for clustering, the RGB data are segmented into pothole and non-pothole objects. The results demonstrate the advantage of complementary processing of low-cost multisensor data, through channeling data streams and linking data processing according to the merits of the individual sensors, for autonomous cost-effective assessment of road-surface conditions using remote sensing technology.

Spectral Optimization of Airborne Multispectral Camera for Land Cover Classification: Automatic Feature Selection and Spectral Band Clustering

Hyperspectral imagery consists of hundreds of contiguous spectral bands. However, most of them are redundant. Thus a subset of well-chosen bands is generally sufficient for a specific problem, enabling to design adapted superspectral sensors dedicated to specific land cover classification. Related both to feature selection and extraction, spectral optimization identifies the most relevant band subset for specific applications, involving a band subset relevance score as well as a method to optimize it. This study first focuses on the choice of such relevance score. Several criteria are compared through both quantitative and qualitative analyses. To have a fair comparison, all tested criteria are compared to classic hyperspectral data sets using the same optimization heuristics: an incremental one to assess the impact of the number of selected bands and a stochastic one to obtain several possible good band subsets and to derive band importance measures out of intermediate good band subsets. Last, a specific approach is proposed to cope with the optimization of bandwidth. It consists in building a hierarchy of groups of adjacent bands, according to a score to decide which adjacent bands must be merged, before band selection is performed at the different levels of this hierarchy.

Building an Integrated Database of Road Design Elements

The road network is the main artery within the city structure, which requires designing of routes and classification within the standards. Hence, the importance of this chapter, which will focus on the standards and design elements of the engineering design of road in terms of road type system, functional classification system, traffic volume system, number of traffic lane system, road width design, side slopes and elevations of road layers, super elevation, design speed, overtaking and stopping sight distance, longitudinal and cross sections of the road path, design elements of horizontal and vertical curves, and intersections. The Civil 3D Land Desktop, GIS programs, and remote sensing technology will be used to design the path of major highway linking two urban areas in Mosul (Northern Iraq), which will be considered a case study. The path of the road and its elements will be designed according to special criteria that are compatible with the topography and nature of the area. The geometric data of the road will then be exported with all the design elements to the GIS program to build an integrated road database. The database is capable of spatial analysis and connectivity with other parts of the road network in the city.

A Review of the Machine Learning in GIS for Megacities Application

Machine learning (ML) is very useful for analyzing data in many domains, including the satellite images processing. In the remote sensing data processing, ML tools are mainly founded out a place for filtering, interpretation and prediction information. Filtering aims at removing noise and performing transformations, which is vital segment of data processing as useful performance of data validation. An interpretation is significant part of it as the stage of objects classification depends of existing task for solution. Prediction is performed to estimate precise values of underlying parameters or future events in the data. It can be used successfully above achievements in a variety of areas. An urbanization is one of the spheres of advance technology application where highly need to collect appropriate data for understanding of challenges facing society. The process of urbanization becomes very important problem, thanks to city expansion. Each city is a complicated system. It consists of various interactive sub-systems and is affected by multiple factors, including population growth, transportation and management policies. To understand the driving forces of the urban structure change, the satellite-based estimates are considered to monitor these changes, in long term. GIS (geographic information system) is equivalent to methods related to the use of geospatial information. Besides, the increasing application of ML techniques in various fields, including GIS, is undeniable. Thus, the chapter attempts to review the application of ML techniques in GIS with a focus on megacities and theirs features fixing/identification and solution.

Clustering Techniques for Land Use Land Cover Classification of Remotely Sensed Images

Image processing is growing fast and persistently. The idea of remotely sensed image clustering is to categorize the image into meaningful land use land cover classes with respect to a particular application. Image clustering is a technique to group an image into units or categories that are homogeneous with respect to one or more characteristics. There are many algorithms and techniques that have been developed to solve image clustering problems, though, none of the method is a general solution. This chapter will highlight the various clustering techniques that bring together the current development on clustering and explores the potentiality of those techniques in extracting earth surface features information from high spatial resolution remotely sensed imageries. It also will provide an insight about the existing mathematical methods and its application to image clustering. Special emphasis will be given on Hölder exponent (HE) and Variance (VAR). HE and VAR are well-established techniques for texture analysis. This chapter will highlight about the Hölder exponent and variance-based clustering method for classifying land use/land cover in high spatial resolution remotely sensed images.

Expanding Navigation Systems by Integrating It with Advanced Technologies

Navigation systems provide the optimized route from one location to another. It is mainly assisted by external technologies such as Global Positioning System (GPS) and satellite-based radio navigation systems. GPS has many advantages such as high accuracy, available anywhere, reliable, and self-calibrated. However, GPS is limited to outdoor operations. The practice of combining different sources of data to improve the overall outcome is commonly used in various domains. GIS is already integrated with GPS to provide the visualization and realization aspects of a given location. Internet of things (IoT) is a growing domain, where embedded sensors are connected to the Internet and so IoT improves existing navigation systems and expands its capabilities. This chapter proposes a framework based on the integration of GPS, GIS, IoT, and mobile communications to provide a comprehensive and accurate navigation solution. In the next section, we outline the limitations of GPS, and then we describe the integration of GIS, smartphones, and GPS to enable its use in mobile applications. For the rest of this chapter, we introduce various navigation implementations using alternate technologies integrated with GPS or operated as standalone devices.