Preface

There are obvious stages of satellite data collection and processing. In general, there are two modes of interaction between remote sensing and geographical information systems (GIS). Remote sensing can be used to generate digital maps that can be integrated into GIS development, whereas GIS data can be applied to interpret and classify remotely sensed data. There is no doubt that it is very important to find out reliable digital sources and point out the proper method for achieving high-accuracy data processing.

Remote sensing and GIS technology are used to improve satellite image processing and classification. Research in this area is linked to numerous factors that affect Earth monitoring such as natural resources, natural disaster observation, urban extension, and intensification of land use and land cover including deforestation, afforestation, land abandonment, and so on. As such, GIS and remote sensing represent useful tools for assessing/evaluating the detection of changes.

In recent years, however, more sophisticated data-driven methods have been used for Earth monitoring because they are more robust and have better capability to handle complicated relationships between input variables. It takes a vital place in use of current technology applications of different machine learning algorithms, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), decision trees (DT), or support vector machines (SVM).

From this point of view, achievements in GIS applications are becoming widely important.

In chapter 1 SAR modeling of geophysics measurements is described for analyzing and modeling SAR interferometric processes in scenarios with different geometric, kinematics, and geological structures as well as for generating pseudo SAR interferograms based on geophysical measurements and topographic maps.

Chapter 2 of this book introduces various navigation implementations using alternate technologies integrated with GPS or operated as standalone devices for expanding navigation systems through combining advanced GIS data processing technologies.

Chapter 3 analyzes machine learning in GIS to develop the megacities application.

In chapter 4, we present research results related to the factors that affect high-accuracy data processing. To begin, we include a study of equatorial plasma bubbles using sky and GPS systems to measure total electron content (TEC) using a GPS receiver and images of the nightglow OI 630.0 nm emissions.

Chapter 5 describes the study of the spectral optimization of an airborne multispectral camera for land cover classification focuses on the choice of such relevance score. Several criteria are compared through both quantitative and qualitative analyses. To achieve 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.

**Chapter 1**

**Abstract**

interferograms

**1. Introduction**

tial interferograms is discussed in [7].

Measurements

InSAR Modeling of Geophysics

*Andon Lazarov, Dimitar Minchev and Chavdar Minchev*

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.

**Keywords:** InSAR, geometry, signal modeling, SAR interferogram, SAR differential

Synthetic aperture radar (SAR) is a coherent microwave imaging instrument capable to provide for data all weather, day and night, guaranteeing global coverage surveillance. SAR interferometry is based on processing two or more complex valued SAR images obtained from different SAR positions [1–4]. The InSAR is a system intends for geophysical measurements and evaluation of topography, slopes, surface deformations (volcanoes, earthquakes, ice fields), glacier studies, vegetation growth, etc. The estimation of topographic height with essential accuracy is performed by the interferometric distance difference measured based on two SAR echoes from the same surface. Changes in topography (displacement), precise to a fraction of a radar wavelength, can be evaluated by differential interferogram generated by three or more successive complex SAR images [5, 6]. Demonstration of time series InSAR processing in Beijing using a small stack of Gaofen-3 differen-

A general overview of the InSAR principles and the recent development of the advanced multi-track InSAR combination methodologies, which allow to discriminate the 3-D components of deformation processes and to follow their temporal evolution, are presented in [8]. The combination of global navigation satellite system (GNSS) and InSAR for future Australian datums is discussed in [9]. A high-precision DEM extraction method based on InSAR data and quality assessment of InSAR DEMs is suggested in [10, 11]. InSAR digital surface model (DSM) and time series analysis based on C-band Sentinel-1 TOPS data are presented in [12, 13]. DEM registration, alignment, and evaluation for SAR interferometry, deformation monitoring by ground-based SAR interferometry (GB-InSAR), a field

Chapter 6 highlights the Hölder exponent and variance-based clustering method for classifying land use/land cover in high spatial resolution, remotely sensed images with clustering techniques.

In Chapter 7, an integrated database of road design elements is used for exporting all the design elements to the GIS program by creating an integrated road database. The achieved database has capability of spatial analysis and connectivity, integrating other parts of the road network in the city.

Chapter 8 presents the results of research using low-cost RGB-D sensors for autonomous pothole detection with spatial fuzzy c-means segmentation. Results demonstrate the advantage of complementary processing of low-cost multisensory 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.

> **Rustam B. Rustamov** EILINK Research and Development Center of Khazar University, Baku, Azerbaijan
