Essential Image Processing and GIS for Remote Sensing is an
accessible overview of the subject and successfully draws together
these three key areas in a balanced and comprehensive manner. The
book provides an overview of essential techniques and a selection
of key case studies in a variety of application areas.
Key concepts and ideas are introduced in a clear and logical
manner and described through the provision of numerous relevant
conceptual illustrations. Mathematical detail is kept to a minimum
and only referred to where necessary for ease of understanding.
Such concepts are explained through common sense terms rather than
in rigorous mathematical detail when explaining image processing
and GIS techniques, to enable students to grasp the essentials of a
notoriously challenging subject area.
The book is clearly divided into three parts, with the first
part introducing essential image processing techniques for remote
sensing. The second part looks at GIS and begins with an overview
of the concepts, structures and mechanisms by which GIS operates.
Finally the third part introduces Remote Sensing Applications.
Throughout the book the relationships between GIS, Image Processing
and Remote Sensing are clearly identified to ensure that students
are able to apply the various techniques that have been covered
appropriately. The latter chapters use numerous relevant case
studies to illustrate various remote sensing, image processing and
GIS applications in practice.
Autorentext
Dr Jian Guo Liu, Senior Lecturer, Department of Earth Sciences and Engineering, Imperial College London, UK
Dr Philippa? Mason, Teaching Associate, Department of Earth Sciences and Engineering, Imperial College London, UK
Klappentext
Essential Image Processing and GIS for Remote Sensing is an accessible overview of the subject and successfully draws together these three key areas in a balanced and comprehensive manner. This book pinpoints the overlap between the individual subjects, providing a summary of essential techniques and a selection of key case studies in a variety of application areas.
The book conveys in-depth knowledge of image processing and GIS in an accessible manner, with clear explanations and conceptual illustrations used throughout to enhance student understanding. The understanding of key concepts is emphasised throughout with minimal assumption of prior mathematical experience.
- Comprehensive coverage of new image processing techniques such as BCET, DDS and SFIM
- Includes case studies based on experience in industry projects using high level image processing and GIS
- Emphasis on easy to understand terminology with clear explanations and conceptual illustrations throughout to enhance student understanding
- Focus on earth science applications
- Extended coverage of essential mathematics for use by course tutors and lecturers highlighted throughout
- Supplementary material is available on the book's companion website: www.wileyeurope.com/college/liu
Inhalt
Overview of the Book xv
Part One Image Processing 1
1 Digital Image and Display 3
1.1 What is a digital image? 3
1.2 Digital image display 4
1.2.1 Monochromatic display 4
1.2.2 Tristimulus colour theory and RGB colour display 5
1.2.3 Pseudo colour display 7
1.3 Some key points 8
Questions 8
2 Point Operations (Contrast Enhancement) 9
2.1 Histogram modification and lookup table 9
2.2 Linear contrast enhancement 11
2.2.1 Derivation of a linear function from two points 12
2.3 Logarithmic and exponential contrast enhancement 13
2.3.1 Logarithmic contrast enhancement 13
2.3.2 Exponential contrast enhancement 14
2.4 Histogram equalization 14
2.5 Histogram matching and Gaussian stretch 15
2.6 Balance contrast enhancement technique 16
2.6.1 *Derivation of coefficients, a, b and c for a BCET parabolic function 16
2.7 Clipping in contrast enhancement 18
2.8 Tips for interactive contrast enhancement 18
Questions 19
3 Algebraic Operations (Multi-image Point Operations) 21
3.1 Image addition 21
3.2 Image subtraction (differencing) 22
3.3 Image multiplication 22
3.4 Image division (ratio) 24
3.5 Index derivation and supervised enhancement 26
3.5.1 Vegetation indices 27
3.5.2 Iron oxide ratio index 28
3.5.3 TM clay (hydrated) mineral ratio index 29
3.6 Standardization and logarithmic residual 29
3.7 Simulated reflectance 29
3.7.1 Analysis of solar radiation balance and simulated irradiance 29
3.7.2 Simulated spectral reflectance image 30
3.7.3 Calculation of weights 31
3.7.4 Example: ATM simulated reflectance colour composite 32
3.7.5 Comparison with ratio and logarithmic residual techniques 33
3.8 Summary 34
Questions 35
4 Filtering and Neighbourhood Processing 37
4.1 Fourier transform: understanding filtering in image frequency 37
4.2 Concepts of convolution for image filtering 39
4.3 Low-pass filters (smoothing) 40
4.3.1 Gaussian filter 41
4.3.2 The k nearest mean filter 42
4.3.3 Median filter 42
4.3.4 Adaptive median filter 42
4.3.5 The k nearest median filter 43
4.3.6 Mode (majority) filter 43
4.3.7 Conditional smoothing filter 43
4.4 High-pass filters (edge enhancement) 44
4.4.1 Gradient filters 45
4.4.2 Laplacian filters 46
4.4.3 Edge-sharpening filters 47
4.5 Local contrast enhancement 48
4.6 *FFT selective and adaptive filtering 48
4.6.1 FFT selective filtering 49
4.6.2 FFT adaptive filtering 51
4.7 Summary 54
Questions 54
5 RGB-IHS Transformation 57
5.1 Colour coordinate transformation 57
5.2 IHS decorrelation stretch 59
5.3 Direct decorrelation stretch technique 61
5.4 Hue RGB colour composites 63
5.5 *Derivation of RGB-IHS and IHS-RGB transformations based on 3D geometry of the RGB colour cube 65
5.5.1 Derivation of RGB-IHS Transformation 65
5.5.2 Derivation of IHS-RGB transformation 66
5.6 *Mathematical proof of DDS and its properties 67
5.6.1 Mathematical proof of DDS 67
5.6.2 The properties of DDS 68
5.7 Summary 70
Questions 70
6 Image Fusion Techniques 71
6.1 RGB-IHS transformation as a tool for data fusion 71
6.2 Brovey transform (intensity modulation) 73
6.3 Smoothing-filter-based intensity modulation 73
6.3.1 The principle of SFIM 74
6.3.2 Merits and limitation of SFIM 75
6.4 Summary 76
Questions 76
7 Principal Component Analysis 77
7.1 Principle of PCA 77
7.2 Principal component images and colour composition 80
7.3 Selective PCA for PC colour composition 82
7.3.1 Dimensionality and colour confusion reduction 82
7.3.2 Spectral contrast mapping 83
7.3.3 FPCS spectral contrast mapping 84
7.4 Decorrelation stretch 85
7.5 Physical-property-orientated coordinate transformation and tasselled cap transformation 85
7.6 Statistic methods for band selection 88
7.6.1 Review of Chavez et al.'s and Sheffield's methods 88
7.6.2 Index of three-dimensionality 89
7.7 Remarks 89
Questions 90
8 Image Classification 91
8.1 Approaches of statistical classification 91
8.1.1 Unsupervised classification 91
8.1.2 Supervised classification 91
8.1.3 Classification processing and imp…