Preface

The advance in space machineries has created a novel technology for observing and monitoring the Earth from space. Most earth observation remote sensing considerations focus on using conventional image processing algorithms or classic edge detection tools. Nevertheless, these techniques do not implement modern physics, applied mathematics, signal communication, remote sensing data, and innovative space technologies. This book provides readers with methods to comprehend how to monitor coastal environments, disaster areas, and infrastructure from space with advanced talent remote sensing technology to bridge the gaps between modern space technology, image processing algorithms, mathematical models and the critical issue of the coastal and infrastructure investigations. In other words, advanced remote sensing technology, which covers sensor developments, and image processing algorithm modifications, which are based on modern physics, artificial intelligence, and machine learning. In these regards, their applications cover a wide range of coastal observations, for instances, high risk of a tsunami depends on the depth of water, the coastal geomorphology, the direction of the tsunami wave, and the existence of rivers or other water canals. In these circumstances, coastal zones are required for new urban planning and specific infrastructure designing to reduce the impact of such a disaster.

In spite of numerous of synthetic aperture radar (SAR) space technology, the developing country researchers and scientists are still focusing on optical remote sensing technology. In fact, microwave remote sensing require use of mathematics and physics behind the SAR technology. The first chapter introduces a new technology for measuring sea surface current using along-track interferometry of TanDEM-X satellite data. This chapter delivers a novel algorithm to retrieve sea surface current using the multichannel MAP height estimator algorithm, which is considered the first study of ocean current in the coastal waters of Peninsular Malaysia.

The available SAR data increases dramatically with the recent operation of many spaceborne and airborne SAR systems. This makes the joint processing of multiple images for accurate understanding and perception of a scene and target possible. For SAR image pairs acquired from different imaging geometries or by different sensors, there is always a geometrical warp between them, which should be compensated first before any deep application. Image registration is aimed to retrieve the warp function to align the same pixel position in each SAR image to the same target position in the global system. A lot of SAR image registration techniques have been developed hitherto. In the second chapter, the algorithms that conduct registration based on image features, such as contour, region, line, and point are accurately addressed. Contour, region, and line, as well as their combinations, are often used for registration of multi-modality images. For SAR images with geometrical distortion and speckle, point feature is generally much clearer and easier extracted. Tie points, corner, and key points are the commonly-used features in SAR image registration. Tie points usually refer to the features extracted from tie patches in SAR image registration.

**II**

**Chapter 7 125**

**Chapter 8 147**

Utilization of Unmanned Aerial Vehicle for Accurate 3D Imaging

Geo Spatial Analysis for Tsunami Risk Mapping *by Abu Bakar Sambah and Fusanori Miura*

*byYoichi Kunii*

Following the second chapter, the third chapter introduces a novel technology for implementation of interferometry synthetic aperture radar (InSAR) with L-band on monitoring harbor. In fact, L-band SAR and its long-lasting temporal coherence is an advantage to perform precise interferometric coherence analysis. In addition, recent high-resolution SAR images are found to be useful for observing relatively small targets, e.g., individual buildings and facilities. In this chapter, author presents the basic theory of SAR observation, interferometric coherence analysis for the disaster monitoring and its examples of the harbor facilities.

However, optical remote sensing experts are relying on commercial software and open source codes without fully understanding the mathematical algorithms involved in image processing. In fact, conventional image processing techniques such as image classification are being used. In this view, classifying remote scenes according to a set of semantic categories is a very challenging problem, because of high intraclass variability and low interclass distance.

The most advanced image processing technique is presented in Chapter 4. One of the advanced learning machine algorithms for image processing is Deep convolutional neural networks (CNNs), which have been widely used to obtain high-level representation in various computer vision tasks. Deep CNN models are trained upon a database of more than 1.2 million categorized natural images of 1000+ classes, which serve as the backbone for many segmentation, detection and classification tasks on other data sets.

The fifth chapter presents an image processing technique that is based on the sub-pixel algorithms for modeling a time series of shoreline changes. In fact, the majority of investigations are only used conventional classification or threshold technique to study short periods of coastal erosion. The novelty of the fifth chapter is that the authors implemented eight years of time series of multispectral data with a subpixel technique to reduce the error of shoreline extraction at sub-pixel, pixel and object-based scales.

A different remote sensing technique is introduced in the sixth chapter for monitoring infrastructure. This technique is based on utilization of accelerometer measurements. In fact, infrastructure, including roads, bridges, tunnels, water supply, sewers, electrical grids, and telecommunications, may be exposed to environmentally-induced or traffic-induced vibrations. Some infrastructure, such as bridges and roadside upright structures, may be sensitive to vibration where accelerometers and other types of sensors may be used for their measurement of sensitivity to environmentally-induced loads, such as wind and earthquakes, and traffic-induced loads, such as passing trucks. With data collected by accelerometers, time histories may be obtained, transformed, and then analyzed to determine their modal frequencies and shapes.

A coastal disaster, which is mainly based on a tsunami disaster, requires such advanced technology of geospatial monitoring of the tsunami risk impact. The seventh chapter delivers the integrated approach of raster weighted overlay of all spatial databases of tsunami vulnerability and risk parameters specifying the vulnerability and risk area due to the tsunami and defines the possible area that could be affected by the tsunami and the potential inundated area.

Finally, the book describes a new technology of Unmanned Aerial Vehicle for accurate three-dimensional reconstruction (3-D). In fact, this technology is rapidly growing among the researchers and scientists. Chapter eight presents a novel technology to construct a precise 3-D image using an Unmanned Aerial Vehicle, which is validated

**V**

by ground field measurements during UAV experiments. In this view, there is great potential to use UAV images for 3-D modeling when compared to operational

I wish to convey my appreciation to all authors who contributed novel work to this book. Without their intense commitment, this book would not have become such a precious piece of novel knowledge. I am also grateful to the IntechOpen editorial team Ms. Martina Josavac and Ms. Maja Bozicevic who afforded the opportunity to publish

**Prof. Dr. Maged Marghany**

Malaysia

Microwave Remote Sensing expert, Faculty Geospatial and Real Estate, Geomatika University College, Kuala Lumpur, WP Kuala Lumpur,

satellite data.

this book.

by ground field measurements during UAV experiments. In this view, there is great potential to use UAV images for 3-D modeling when compared to operational satellite data.

I wish to convey my appreciation to all authors who contributed novel work to this book. Without their intense commitment, this book would not have become such a precious piece of novel knowledge. I am also grateful to the IntechOpen editorial team Ms. Martina Josavac and Ms. Maja Bozicevic who afforded the opportunity to publish this book.

## **Prof. Dr. Maged Marghany**

Microwave Remote Sensing expert, Faculty Geospatial and Real Estate, Geomatika University College, Kuala Lumpur, WP Kuala Lumpur, Malaysia

**IV**

Following the second chapter, the third chapter introduces a novel technology for implementation of interferometry synthetic aperture radar (InSAR) with L-band on monitoring harbor. In fact, L-band SAR and its long-lasting temporal coherence is an advantage to perform precise interferometric coherence analysis. In addition, recent high-resolution SAR images are found to be useful for observing relatively small targets, e.g., individual buildings and facilities. In this chapter, author presents the basic theory of SAR observation, interferometric coherence analysis for the

However, optical remote sensing experts are relying on commercial software and open source codes without fully understanding the mathematical algorithms involved in image processing. In fact, conventional image processing techniques such as image classification are being used. In this view, classifying remote scenes according to a set of semantic categories is a very challenging problem, because of high intraclass vari-

The most advanced image processing technique is presented in Chapter 4. One of the advanced learning machine algorithms for image processing is Deep convolutional neural networks (CNNs), which have been widely used to obtain high-level representation in various computer vision tasks. Deep CNN models are trained upon a database of more than 1.2 million categorized natural images of 1000+ classes, which serve as the backbone for many segmentation, detection and classification tasks on other

The fifth chapter presents an image processing technique that is based on the sub-pixel algorithms for modeling a time series of shoreline changes. In fact, the majority of investigations are only used conventional classification or threshold technique to study short periods of coastal erosion. The novelty of the fifth chapter is that the authors implemented eight years of time series of multispectral data with a subpixel technique to reduce the error of shoreline extraction at sub-pixel, pixel and object-based scales.

A different remote sensing technique is introduced in the sixth chapter for monitoring infrastructure. This technique is based on utilization of accelerometer measurements. In fact, infrastructure, including roads, bridges, tunnels, water supply, sewers, electrical grids, and telecommunications, may be exposed to environmentally-induced or traffic-induced vibrations. Some infrastructure, such as bridges and roadside upright structures, may be sensitive to vibration where accelerometers and other types of sensors may be used for their measurement of sensitivity to environmentally-induced loads, such as wind and earthquakes, and traffic-induced loads, such as passing trucks. With data collected by accelerometers, time histories may be obtained, transformed,

A coastal disaster, which is mainly based on a tsunami disaster, requires such advanced technology of geospatial monitoring of the tsunami risk impact. The seventh chapter delivers the integrated approach of raster weighted overlay of all spatial databases of tsunami vulnerability and risk parameters specifying the vulnerability and risk area due to the tsunami and defines the possible area that could be affected by the tsunami

Finally, the book describes a new technology of Unmanned Aerial Vehicle for accurate three-dimensional reconstruction (3-D). In fact, this technology is rapidly growing among the researchers and scientists. Chapter eight presents a novel technology to construct a precise 3-D image using an Unmanned Aerial Vehicle, which is validated

and then analyzed to determine their modal frequencies and shapes.

disaster monitoring and its examples of the harbor facilities.

ability and low interclass distance.

and the potential inundated area.

data sets.

Section 1

Synthetic Aperture Radar

Image Processing and

Applications

1

## Section 1
