
Image Processing and GIS for Remote Sensing
Beschreibung
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
Weitere Details
Weitere Ausgaben
Andere Ausgaben


Personen
Inhalt
Overview of the Book xi
Part I Image processing
1 Digital image and display 3
1.1 What is a digital image? 3
1.2 Digital image display 4
1.3 Some key points 8
1.4 Questions 8
2 Point operations (contrast enhancement) 9
2.1 Histogram modification and lookup table 9
2.2 Linear contrast enhancement (LCE) 11
2.3 Logarithmic and exponential contrast enhancement 13
2.4 Histogram equalisation (HE) 14
2.5 Histogram matching (HM) and Gaussian stretch 15
2.6 Balance contrast enhancement technique (BCET) 16
2.7 Clipping in contrast enhancement 18
2.8 Tips for interactive contrast enhancement 18
2.9 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) 22
3.5 Index derivation and supervised enhancement 26
3.6 Standardization and logarithmic residual 29
3.7 Simulated reflectance 29
3.8 Summary 33
3.9 Questions 34
4 Filtering and neighbourhood processing 35
4.1 FT: Understanding filtering in image frequency 35
4.2 Concepts of convolution for image filtering 37
4.3 Low pass filters (smoothing) 38
4.4 High pass filters (edge enhancement) 42
4.5 Local contrast enhancement 45
4.6 FFT selective and adaptive filtering 46
4.7 Summary 52
4.8 Questions 52
5 RGB-IHS transformation 55
5.1 Colour co-ordinate transformation 55
5.2 IHS de-correlation stretch 57
5.3 Direct de-correlation stretch technique 58
5.4 Hue RGB colour composites 60
5.5 Derivation of RGB-IHS and IHS-RGB transformation based on 3D geometry of the RGB colour cube 63
5.6 Mathematical proof of DDS and its properties 65
5.7 Summary 67
5.8 Questions 67
6 Image fusion techniques 69
6.1 RGB-IHS transformation as a tool for data fusion 69
6.2 Brovey transform (intensity modulation) 71
6.3 Smoothing filter-based intensity modulation 71
6.4 Summary 75
6.5 Questions 75
7 Principal component analysis 77
7.1 Principle of the PCA 77
7.2 PC images and PC colour composition 79
7.3 Selective PCA for PC colour composition 82
7.4 De-correlation stretch 84
7.5 Physical property orientated coordinate transformation and tasselled cap transformation 85
7.6 Statistical methods for band selection 87
7.7 Remarks 88
7.8 Questions 89
8 Image classification 91
8.1 Approaches of statistical classification 91
8.2 Unsupervised classification (iterative clustering) 92
8.3 Supervised classification 96
8.4 Decision rules: Dissimilarity functions 97
8.5 Post-classification processing: Smoothing and accuracy assessment 98
8.6 Summary 101
8.7 Questions 101
9 Image geometric operations 103
9.1 Image geometric deformation 103
9.2 Polynomial deformation model and image warping co-registration 106
9.3 GCP selection and automation of image co-registration 109
9.4 Summary 110
9.5 Questions 110
10 Introduction to interferometric synthetic aperture radar technique 113
10.1 The principle of a radar interferometer 113
10.2 Radar interferogram and DEM 115
10.3 Differential InSAR and deformation measurement 117
10.4 Multi-temporal coherence image and random change detection 119
10.5 Spatial de-correlation and ratio coherence technique 121
10.6 Fringe smoothing filter 123
10.7 Summary 124
10.8 Questions 125
11 Sub-pixel technology and its applications 127
11.1 Phase correlation algorithm 127
11.2 PC scanning for pixel-wise disparity estimation 132
11.3 Pixel-wise image co-registration 134
11.4 Very narrow-baseline stereo matching and 3D data generation 139
11.5 Ground motion/deformation detection and estimation 143
11.6 Summary 146
Part II Geographical information systems
12 Geographical information systems 151
12.1 Introduction 151
12.2 Software tools 152
12.3 GIS cartography and thematic mapping 152
12.4 Standards, inter-operability and metadata 153
12.5 GIS and the internet 154
13 Data models and structures 155
13.1 Introducing spatial data in representing geographic features 155
13.2 How are spatial data different from other digital data? 155
13.3 Attributes and measurement scales 156
13.4 Fundamental data structures 156
13.5 Raster data 157
13.6 Vector data 161
13.7 Data conversion between models and structures 171
13.8 Summary 174
13.9 Questions 175
14 Defining a coordinate space 177
14.1 Introduction 177
14.2 Datums and projections 177
14.3 How coordinate information is stored and accessed 188
14.4 Selecting appropriate coordinate systems 189
14.5 Questions 189
15 Operations 191
15.1 Introducing operations on spatial data 191
15.2 Map algebra concepts 192
15.3 Local operations 194
15.4 Neighbourhood operations 199
15.5 Vector equivalents to raster map algebra 206
15.6 Automating GIS functions 209
15.7 Summary 209
15.8 Questions 210
16 Extracting information from point data: Geostatistics 211
16.1 Introduction 211
16.2 Understanding the data 211
16.3 Interpolation 214
16.4 Summary 224
16.5 Questions 225
17 Representing and exploiting surfaces 227
17.1 Introduction 227
17.2 Sources and uses of surface data 227
17.3 Visualising surfaces 230
17.4 Extracting surface parameters 236
17.5 Summary 245
17.6 Questions 246
18 Decision support and uncertainty 247
18.1 Introduction 247
18.2 Decision support 247
18.3 Uncertainty 248
18.4 Risk and hazard 250
18.5 Dealing with uncertainty in GIS-based spatial analysis 250
18.6 Summary 254
18.7 Questions 255
19 Complex problems and multi-criterion evaluation 257
19.1 Introduction 257
19.2 Different approaches and models 258
19.3 Evaluation criteria 259
19.4 Deriving weighting coefficients 260
19.5 Multi-criterion combination methods 263
19.6 Summary 272
19.7 Questions 272
Part III Remote sensing applications
20 Image processing and GIS operation strategy 275
20.1 General image processing strategy 276
20.2 Remote sensing-based GIS projects: From images to thematic mapping 284
20.3 An example of thematic mapping based on optimal visualisation and interpretation of multi-spectral satellite imagery 284
20.4 Summary 292
21 Thematic teaching case studies in SE Spain 293
21.1 Thematic information extraction (1): Gypsum natural outcrop mapping and quarry change assessment 293
21.2 Thematic information extraction (2): Spectral enhancement and mineral mapping of epithermal gold alteration and iron-ore deposits in ferroan dolomite 299
21.3 Remote sensing and GIS: Evaluating vegetation and landuse change in the Nijar Basin, SE Spain 308
21.4 Applied remote sensing and GIS: A combined interpretive tool for regional tectonics, drainage and water resources in the Andarax basin 318
22 Research case studies 335
22.1 Vegetation change in the Three Parallel Rivers region, Yunnan Province, China 335
22.2 GIS modelling of earthquake damage zones using satellite imagery and digital elevation model (DEM) data 345
22.3 Predicting landslides using fuzzy geohazard mapping: An example from Piemonte, north-west Italy 369
22.4 Land surface change detection in a desert area in Algeria using multi-temporal ERS SAR coherence images 380
23 Industrial case studies 389
23.1 Multi-criteria assessment of mineral prospectivity in SE Greenland 389
23.2 Water resource exploration in Somalia 405
Part IV Summary
24 Concluding remarks 419
24.1 Image processing 419
24.2 Geographic Information Systems 422
24.3 Final remarks 425
Appendix A Imaging sensor systems and remote sensing satellites 427
A.1 Multi-spectral sensing 427
A.2 Broadband multi-spectral sensors 431
A.3 Thermal sensing and TIR sensors 434
A.4 Hyperspectral sensors (imaging spectrometers) 434
A.5 Passive microwave sensors 436
A.6 Active sensing: SAR imaging systems 437
Appendix B Online resources for information software and data 441
B.1 Software - proprietary, low cost and free (shareware) 441
B.2 Information and technical information on standards, best practice, formats, techniques and various publications 441
B.3 Data sources including online satellite imagery from major suppliers, DEM data plus GIS maps and data of all kinds 442
References 443
Index 451
CHAPTER 1
Digital image and display
1.1 What is a digital image?
An image is a picture, photograph or any form of a two-dimensional (2D) representation of objects or a scene. The information in an image is presented in tones or colours. A digital image is a two-dimensional array of numbers. Each cell of a digital image is called a pixel, and the number representing the brightness of the pixel is called a digital number (DN) (Fig. 1.1). As a 2D array, a digital image is composed of data in lines and columns. The position of a pixel is allocated with the line and column of its DN. Such regularly arranged data, without x and y coordinates, are usually called raster data. As digital images are nothing more than data arrays, mathematical operations can be readily performed on the digital numbers of images. Mathematical operations on digital images are called digital image processing.
Digital image data can also have a third dimension: layers (Fig. 1.1). Layers are the images of the same scene but containing different information. In multi-spectral images, layers are the images of different spectral ranges called bands or channels. For instance, a colour picture taken by a digital camera is composed of three bands containing red, green and blue spectral information individually. The term band is more often used than layer to refer to multi-spectral images. Generally speaking, geometrically registered multi-dimensional data sets of the same scene can be considered as layers of an image. For example, we can digitise a geological map and then co-register the digital map with a Landsat TM image. Then the digital map becomes an extra layer of the scene beside the seven TM spectral bands. Similarly, if we have a dataset of digital elevation model (DEM) to which a SPOT image is rectified, then the DEM can be considered as a layer of the SPOT image beside its four spectral bands. In this sense, we can consider a set of co-registered digital images as a three-dimensional (3D) dataset and with the 'third' dimension providing the link between image processing and GIS.
Fig. 1.1 A digital image and its elements.
A digital image can be stored as a file in a computer data store on a variety of media, such as a hard disk, memory stick, CD, etc. It can be displayed in black and white or in colour on a computer monitor as well as in hard copy output such as film or print. It may also be output as a simple array of numbers for numerical analysis. As a digital image, its advantages include:
- The images do not change with environmental factors as hard copy pictures and photographs do;
- the images can be identically duplicated without any change or loss of information;
- the images can be mathematically processed to generate new images without altering the original images;
- the images can be electronically transmitted from or to remote locations without loss of information.
Remotely sensed images are acquired by sensor systems onboard aircraft or spacecraft, such as earth observation satellites. The sensor systems can be categorised into two major branches: passive sensors and active sensors. Multi-spectral optical imaging systems are passive sensors that use solar radiation as the principal source of illumination for imaging. Typical examples include across-track and push-broom multi-spectral scanners, and digital cameras. An active sensor system provides its own means of illumination for imaging, such as synthetic aperture radar (SAR). Details of major remote sensing satellites and their sensor systems are beyond the scope of this book, but we provide a summary in Appendix A for your reference.
1.2 Digital image display
We live in a world of colour. The colours of objects are the result of selective absorption and reflection of electromagnetic radiation from illumination sources. Perception by the human eye is limited to the spectral range of 0.38-0.75 µm, that is, a very small part of the solar spectral range. The world is actually far more colourful than we can see. Remote sensing technology can record over a much wider spectral range than human visual ability, and the resultant digital images can be displayed as either black and white or colour images using an electronic device such as a computer monitor. In digital image display, the tones or colours are visual representations of the image information recorded as digital image DNs, but they do not necessarily convey the physical meanings of these DNs. We will explain this further in our discussion on false colour composites later.
The wavelengths of major spectral regions used for remote sensing are listed below:
Visible light (VIS): 0.4-0.7 µm Blue (B) 0.4-0.5 µm Green (G) 0.5-0.6 µm Red (R) 0.6-0.7 µm Visible-photographic infrared: 0.5-0.9 µm Reflective infrared (IR): 0.7-3.0 µm Nearer infrared (NIR) 0.7-1.3 µm Short-wave infrared (SWIR) 1.3-3.0 µm Thermal infrared (TIR): 3-5 µm, 8-14 µm Microwave: 0.1-100 cmCommonly used abbreviations of the spectral ranges are denoted by the letters in the brackets in the list above. The spectral range covering visible light and nearer infrared is the most popular for broadband multi-spectral sensor systems and it is usually denoted as VNIR.
1.2.1 Monochromatic display
Any image, either a panchromatic image or a spectral band of a multi-spectral image, can be displayed as a black and white (B/W) image by a monochromatic display. The display is implemented by converting DNs to electronic signals in a series of energy levels that generate different grey tones (brightness) from black to white, and thus to formulate a B/W image display. Most image processing systems support an 8 bits graphical display, which corresponds to 256 grey levels and displays DNs from 0 (black) to 255 (white). This display range is wide enough for human visual capability. It is also sufficient for some of the more commonly used remotely sensed images, such as Landsat TM/ETM+, SPOT HRV and Terra-1 ASTER VIR-SWIR (see Appendix A); the DN ranges of these images are not wider than 0-255. On the other hand, many remotely sensed images have much wider DN ranges than 8 bits, such as Ikonos and QuickBird, whose images have an 11 bits DN range (0-2047), and Landsat 8 Operational Land Imager (OLI), of 12 bits. In this case, the images can still be visualised in an 8-bit display device in various ways, such as by compressing the DN range into 8 bits or displaying the image in scenes of several 8-bit intervals of the whole DN range. Many sensor systems offer wide dynamic ranges to ensure that the sensors can record across all levels of radiation energy without localised sensor adjustment. Since the received solar radiation does not normally vary significantly within an image scene of limited size, the actual DN range of the scene is usually much narrower than the full dynamic range of the sensor and thus can be well adapted into an 8-bit DN range for display.
In a monochromatic display of a spectral band image, the brightness (grey level) of a pixel is proportional to the reflected energy in this band from the corresponding ground area. For instance, in a B/W display of a red band image, light red appears brighter than dark red. This is also true for invisible bands (e.g. infrared bands), though the 'colours' cannot be seen. After all, any digital image is composed of DNs; the physical meaning of DNs depends on the source of the image. A monochromatic display visualises DNs in grey tones from black to white, while ignoring the physical relevance.
1.2.2 Tristimulus colour theory and RGB (red, green, blue) colour display
If you understand the structure and principle of a colour TV tube, you must know that the tube is composed of three colour guns of red, green and blue. These three colours are known as primary colours. The mixture of the lights of these three primary colours can produce any colour on a TV. This property of the human perception of colour can be explained by the tristimulus colour theory. The human retina has three types of cones and the response by each type of cone is a function of the wavelength of the incident light; they peak at 440 nm (blue), 545 nm (green) and 680 nm (red). In other words, each type of cone is primarily sensitive to one of the primary colours: blue, green or red. A colour perceived by a person depends on the proportion of each of these three types of cones being stimulated and thus can be expressed as a triplet of numbers (r, g, b) even though visible light is electromagnetic radiation in a continuous spectrum of 380-750 nm. A light of non-primary colour C will stimulate different portions of each cone type to...
Systemvoraussetzungen
Dateiformat: ePUB
Kopierschutz: Adobe-DRM (Digital Rights Management)
Systemvoraussetzungen:
- Computer (Windows; MacOS X; Linux): Installieren Sie bereits vor dem Download die kostenlose Software Adobe Digital Editions (siehe E-Book Hilfe).
- Tablet/Smartphone (Android; iOS): Installieren Sie bereits vor dem Download die kostenlose App Adobe Digital Editions oder die App PocketBook (siehe E-Book Hilfe).
- E-Book-Reader: Bookeen, Kobo, Pocketbook, Sony, Tolino u.v.a.m. (nicht Kindle)
Das Dateiformat ePUB ist sehr gut für Romane und Sachbücher geeignet – also für „fließenden” Text ohne komplexes Layout. Bei E-Readern oder Smartphones passt sich der Zeilen- und Seitenumbruch automatisch den kleinen Displays an.
Mit Adobe-DRM wird hier ein „harter” Kopierschutz verwendet. Wenn die notwendigen Voraussetzungen nicht vorliegen, können Sie das E-Book leider nicht öffnen. Daher müssen Sie bereits vor dem Download Ihre Lese-Hardware vorbereiten.
Bitte beachten Sie: Wir empfehlen Ihnen unbedingt nach Installation der Lese-Software diese mit Ihrer persönlichen Adobe-ID zu autorisieren!
Weitere Informationen finden Sie in unserer E-Book Hilfe.