Computer Processing of Remotely-Sensed Images

Paul M. Mather, Magaly Koch, Magaly Koch

• Publisher: Wiley-Blackwell
• Year: 2022
• Print ISBN: 9781119502821
• eBook ISBN: 9781119502975
• Edition: 5
• Topic: Technology & Engineering
• Subtopic: Civil Engineering
• Index: Technology & Engineering

Computer Processing of Remotely-Sensed Images

A thorough introduction to computer processing of remotely-sensed images, processing methods, and applications

Remote sensing is a crucial form of measurement that allows for the gauging of an object or space without direct physical contact, allowing for the assessment and recording of a target under conditions which would normally render access difficult or impossible. This is done through the analysis and interpretation of electromagnetic radiation (EMR) that is reflected or emitted by an object, surveyed and recorded by an observer or instrument that is not in contact with the target. This methodology is particularly of importance in Earth observation by remote sensing, wherein airborne or satellite-borne instruments of EMR provide data on the planet's land, seas, ice, and atmosphere. This permits scientists to establish relationships between the measurements and the nature and distribution of phenomena on the Earth's surface or within the atmosphere.

Still relying on a visual and conceptual approach to the material, the fifth edition of this successful textbook provides students with methods of computer processing of remotely sensed data and introduces them to environmental applications which make use of remotely-sensed images. The new edition's content has been rearranged to be more clearly focused on image processing methods and applications in remote sensing with new examples, including material on the Copernicus missions, microsatellites and recently launched SAR satellites, as well as time series analysis methods.

The fifth edition of Computer Processing of Remotely-Sensed Images also contains:

• A cohesive presentation of the fundamental components of Earth observation remote sensing that is easy to understand and highly digestible
• Largely non-technical language providing insights into more advanced topics that may be too difficult for a non-mathematician to understand
• Illustrations and example boxes throughout the book to illustrate concepts, as well as revised examples that reflect the latest information
• References and links to the most up-to-date online and open access sources used by students

Computer Processing of Remotely-Sensed Images is a highly insightful textbook for advanced undergraduates and postgraduate students taking courses in remote sensing and GIS in Geography, Geology, and Earth & Environmental Science departments.

1
Remote Sensing: Basic Principles

Electromagnetic radiation is just basically mysterious.

B.K. Ridley, Time, Space and Things, 2nd edition.

Cambridge University Press, Cambridge, 1984.

1.1 Introduction

The science of remote sensing consists of the collection, analysis, and interpretation of measurements of the magnitude of electromagnetic radiation (EMR) that is reflected from or emitted by a target and observed or recorded from a vantage point by an observer or instrument that is not in contact with the target. Earth observation (EO) by remote sensing is the interpretation and understanding of measurements made by airborne or satellite‐borne instruments of EMR that is reflected from or emitted by objects on the Earth's land, ocean, or ice surfaces or within the atmosphere, together with the establishment of relationships between these measurements and the nature and distribution of phenomena on the Earth's surface or within the atmosphere. Figure 1.1 shows in schematic form the various methods of computer processing (blue boxes) that generate products (green boxes) from remotely sensed data.

Figure 1.1 Uses of remotely sensed data. The green boxes show the products derived from remotely sensed data, such as image maps and classified images. The blue boxes show the computer processing techniques that are used to derive these products. Image maps are frequently used as backdrops in a GIS, whereas the process of pattern recognition produces labelled (nominal scale) images showing the distribution of individual Earth surface cover types. Quantitative measures such as vegetation indices are derived from calibrated data and are often linked via regression analysis to Earth surface properties such as sea surface temperature or soil moisture content. The computer processing techniques to extract and analyse remotely sensed data are presented in the remainder of this book.

This book is concerned with methods of input, computer processing, and output of digital EO data. It is technique oriented, so that questions such as ‘how do I do that?’ take precedence over subject‐matter oriented (‘what does that result mean?’). Naturally, it is impossible to answer the technique‐oriented questions without some considerable appreciation of the meaning of the data that is being processed and vice versa. The authors therefore assume that a course based on this book is augmented by further courses that deal with applications. These courses can run simultaneously or sequentially. Bringing in real data describing substantial examples is important when teaching practical methodology so that the student experiences the difficulties of experimental design and comprehension of program documentation as well as the morale‐reducing moments when it is realised that the technique has not provided the required answers.

This chapter introduces the principles of remote sensing covering the introductory aspects of the interaction between EMR and the Earth's surface and atmosphere. This is followed by a survey of computer processing methods applied to remotely sensed profiles and images, ranging from simple arithmetic means, medians, and ratios to methods like machine learning that are based on the discipline of computer science or, more specifically, artificial intelligence (AI).

Remotely sensed images are sometimes used as image maps or backcloths for the display of spatial data in a Geographical Information System (GIS). Methods of improving the appearance and interpretability of an image or backcloth (termed enhancement procedures) are dealt with in Chapters 3, 4, and 6. Chapter 7 is an introduction to classification (pattern recognition) techniques that produce labelled images in which each category of land use, for example, is represented by a numerical label (for example, 1 = broad‐leaved forest, 2 = water, and so on.). These labelled images can provide free‐standing information or can be combined with other spatial data within a GIS. Properties of Earth surface materials, such as soil moisture content, sea surface temperature (SST), or biomass can be related to remotely sensed measurements using statistical methods. For instance, a sample of measurements of soil moisture content can be collected close to the time of satellite overpass, and the corresponding ground reflectance or surface temperature values that are recorded by the satellite's instruments can be related via regression analysis to the field measurements. This sample relationship can then be applied to the entire image area of interest. These biogeophysical variables are used in environmental modelling, often within a GIS. Elevation models are another form of remotely sensed spatial information that is used in a GIS. Digital elevation models (DEMs) can be derived from optical imagery using two sensors, for example one pointing down and one pointing obliquely backwards (this is the case with the Advanced Spaceborne Thermal Emission and Reflective Spectrometer [ASTER] sensor, discussed in Chapter 2). Another way of producing elevation models is by the use of synthetic aperture radar (SAR) interferometry, which is mentioned in Chapter 2 and dealt with in more detail in Chapter 8. The increasing cooperation between remote sensing specialists and GIS users means that more products are available to GIS users and the more spatial information is combined with remotely sensed data to produce improved results. This is an example of synergy (literally, working together).

A fundamental principle underlying the use of remotely sensed data is that different objects on the Earth's surface and in the atmosphere reflect, absorb, transmit, or emit electromagnetic energy in different proportions across the range of wavelengths known as the electromagnetic spectrum, and that such differences allow these objects to be identified uniquely. Sensors mounted on aircraft or satellite platforms record the magnitude of the energy flux reflected from or emitted by objects on the Earth's surface. These measurements are made at a large number of points distributed either along a one‐dimensional profile on the ground below the platform or over a two‐dimensional area below or to one side of the ground track of the platform. Figure 1.2a show...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright Page
• In Memoriam
• Preface to the First Edition
• Preface to the Second Edition
• Preface to the Third Edition
• Preface to the Fourth Edition
• Preface to the Fifth Edition
• List of Examples
• 1 Remote Sensing
• 2 Remote Sensing Platforms and Sensors
• 3 Preprocessing of Remotely Sensed Data
• 4 Image Enhancement Techniques
• 5 Image Transforms
• 6 Filtering Techniques
• 7 Classification
• 8 Advanced Topics
• Appendix A Computing for Remote Sensing
• Index
• End User License Agreement