Table of Contents
Cover
Title
Copyright
Preface to the First Edition
Preface to the Second Edition
Preface to the Third Edition
Preface to the Fourth Edition
List of Examples
web page
1 Remote Sensing: Basic Principles
1.1 Introduction
1.2 Electromagnetic Radiation and Its Properties
1.3 Interaction with Earth-Surface Materials
1.4 Summary
2 Remote Sensing Platforms and Sensors
2.1 Introduction
2.2 Charahcteristics of Imaging Remote Sensing Instruments
2.3 Optical, Near-infrared and Thermal Imaging Sensors
2.4 Microwave Imaging Sensors
2.5 Summary
3 Hardware and Software Aspects of Digital Image Processing
3.1 Introduction
3.2 Properties of Digital Remote Sensing Data
3.3 Numerical Analysis and Software Accuracy
3.4 Some Remarks on Statistics
3.5 Summary
4 Preprocessing of Remotely-Sensed Data
4.1 Introduction
4.2 Cosmetic Operations
4.3 Geometric Correction and Registration
4.4 Atmospheric Correction
4.5 Illumination and View Angle Effects
4.6 Sensor Calibration
4.7 Terrain Effects
4.8 Summary
5 Image Enhancement Techniques
5.1 Introduction
5.2 Human Visual System
5.3 Contrast Enhancement
5.4 Pseudocolour Enhancement
5.5 Summary
6 Image Transforms
6.1 Introduction
6.2 Arithmetic Operations
6.3 Empirically Based Image Transforms
6.4 Principal Components Analysis
6.5 Hue-Saturation-Intensity (HSI) Transform
6.6 The Discrete Fourier Transform
6.7 The Discrete Wavelet Transform
6.8 Change Detection
6.9 Image Fusion
6.10 Summary
7 Filtering Techniques
7.1 Introduction
7.2 Spatial Domain Low-Pass (Smoothing) Filters
7.3 Spatial Domain High-Pass (Sharpening) Filters
7.4 Spatial Domain Edge Detectors
7.5 Frequency Domain Filters
7.6 Summary
8 Classification
8.1 Introduction
8.2 Geometrical Basis of Classification
8.3 Unsupervised Classification
8.4 Supervised Classification
8.5 Subpixel Classification Techniques
8.6 More Advanced Approaches to Image Classification
8.7 Incorporation of Non-spectral Features
8.8 Contextual Information
8.9 Feature Selection
8.10 Classification Accuracy
8.11 Summary
9 Advanced Topics
9.1 Introduction
9.2 SAR Interferometry
9.3 Imaging Spectroscopy
9.4 Lidar
9.5 Summary
10 Environmental Geographical Information Systems: A Remote Sensing Perspective
10.1 Introduction
10.2 Data Models, Data Structures and File Formats
10.3 Geodata Processing
10.4 Locational Analysis
10.5 Spatial Analysis
10.6 Environmental Modelling
10.7 Visualization
10.8 Multicriteria Decision Analysis of Groundwater Recharge Zones
10.9 Assessing Flash Flood Hazards by Classifying Wadi Deposits in Arid Environments
10.10 Remote Sensing and GIS in Archaeological Studies
Appendix A Accessing MIPS
Appendix B Getting Started with MIPS
Appendix C Description of Sample Image Datasets
Appendix D Acronyms and Abbreviations
References
Index
End User License Agreement
List of Tables
1 Remote Sensing: Basic Principles
Table 1.1 Terms and symbols used in measurement.
Table 1.2 Wavebands corresponding to perceived colours of visible light.
Table 1.3 Radar wavebands and nomenclature.
2 Remote Sensing: Basic Principles
Table 2.1 Entropy by band for Landsat TM and MSS sensors based on Landsat-4 image of ChesapeakeBayarea, 2 November 1982 (sceneE-40109–15140). Seetextforexplanation.
Table 2.2 The AVHRR/3 Instrument carried by the NOAA satellites.
Table 2.3 MODIS wavebands and key uses. Bands 13 and 14 operate in high lowgain mode. Bands 21 and 22 have the wavelength range but band 21 saturates at about 500 K, whereas band 22 saturates at about 335K.
Table 2.4 Landsat Data Continuity Mission: Operational Land Imager bands.
Table 2.5 Spatial resolution and swath widths for the SPOT-5 instruments HRG (High Resolution Geometric), Vegetation-2 and HRS (High Resolution Stereoscopic) instruments carried by SPOT-5. Note that2.5 m panchromatic imagery is obtained byprocessing the 5 m data using a technique called ‘supermode’ (see text for details).
Table 2.6 ASTER spectral bands. The ASTER dataset is subdivided into three parts VNIR (Visible and Near Infra-Red), SWIR (Short Wave Infra-Red) and TIR (Thermal Infra-Red). The spatial resolution of each subset is: VNIR 15m, SWIR 30m and TIR 90m. The swath width is 60km. Data in bands 1 – 9 are quantized using 256 levels (8 bits). The TIR bands use 12 bit quantization.
Table 2.7 Maximum radiance for different gain settings for the ASTER VNIR and SWIR spectral bands.
Table 2.8 Radar wavebands and nomenclature.
Table 2.9 Synthetic Aperture Radar tutorial resources on the Internet.
Table 2.10 Radarsat-2 modes, spatial resolutions and orbit characteristics.
3 Hardware and Software Aspects of Digital Image Processing
Table 3.1 Combinations of the primary colours of light (red, green and blue) combine to produce intermediate colours such as purple and orange. Where the values of the three primary colours are equal, the result is a shade of grey between black and white. The intensities shown assume 8-bit representation, that is a 0–255 scale.
Table 3.2 Different dynamic ranges used to represent remotely sensed image data.
Table 3.3 Edited extract from ASTER metadata file, generated by MIPS.
Table 3.4 Example of computational error in matrix inversion. The element (3,3) of the Initial Data Matrix is changed from 10.0 to 9.99 and the solution (Inverse Matrix) changes considerably (by more than 5%) as a result. In the two cases, the result of multiplying the input matrix (Initial Data Matrix or the Perturbed Data Matrix) by the computed inverse is shown. The resulting matrix (listed as Initial Data Matrix × Inverse or Perturbed Matrix × Inverse) should approximate to the Identity Matrix (consisting of values of 1.0 along the principal diagonal and 0.0 elsewhere).
4 Preprocessing of Remotely-Sensed Data
Table 4.1 Example of histogram matching for de-striping Landsat MSS and TM images.
Table 4.2 Matrix P and vectors e and a required in solution of second-order least-squares estimation procedure.
Table 4.3 Landsat-5 TM calibration coefficients from Thome et al. (1993). G
i
is thegain value for band iand D is the number of days since the launch of Landsat-5 (1 March 1984).
Table 4.4 Landsat-5 TM offset (ao) and gain (ai
)
coefficients.
Table 4.5 Extract from SPOT header file showing radiometric gains and offsets.
Table 4.6 Maximum radiance for different gain settings for the ASTER VNIR and SWIR spectral bands.
Table 4.7 Exo-atmospheric solar irradiance for (a) Landsat TM, (b) Landsat ETM+, (c) SPOT HRV (XS) bands and ASTER (Markham and Barker, 1987; Price, 1988; Teilletand Fedosejevs, 1995; Irish, 2008 Thome, personal communication). The centre wavelength is expressed in micrometres (−im) and the exo-atmospheric solar irradiance in mWcm
−2
sr
−1
|im
−1
. See also Guyot and Gu (1994), Table 2.
5 Image Enhancement Techniques
Table 5.1 Illustrating calculations involved in histogram equalization procedure. N= 262 144, n
t
= 16384. See text for explanation.
Table 5.2 Number of pixels allocated to each class after the application of the equalisation procedure shown in Figure 5.1a. Note that the smaller classes in the input have been amalgamated, reducing the contrast in those areas, while larger classes are more widely spaced, givinggreater contrast. The number of pixels allocated to each non-empty class varies considerably, because discrete input classes cannot logically be split into subclasses.
Table 5.3 Fitting observed histogram of pixel values to a Gaussian histogram. See text for discussion.
Table 5.4 Number of pixels at each level following transformation to Gaussian model.
6 Image Transforms
Table 6.1 Coefficients for the Tasselled Cap functions ‘brightness’, ‘greenness’ and ‘wetness’ for Landsat Thematic Mapper bands 1–5 and 7.
Table 6.2 Correlations among Thematic Mapper reflective bands (1–5 and 7) for the Littleport TM image. The means and standard deviations of the six bands are shown in the rightmost two columns.
Table 6.3 Principal component loadings for the six principal components of the Littleport TM image. Note that the sum of squares of the loadings for a given principal components (column) is equal to the eigenvalue. The percent variance value is obtained by dividing the eigenvalue by the total variance (six in this case because standardized components are used - see text) and multiplying by 100.
Table 6.4 Variance-covariance matrix for the Littleport TM image set. The last row shows the variance of the corresponding band expressed as a percentage of the total variance of the image set. The expected variance for each band is 16.66%, but the variance ranges from 2.61% for band 2 to 47.56% for band 4.
Table 6.5 Principal component loadings for the six principal components of the Littleport TM image, based on the covariance matrix shown in Table 6.3a.
Table 6.6 Number of operations required to compute the Fourier transform coefficients a and b for a series of length N (column (i)) using least-squares methods (column (ii)) and the Fast Fourier Transform (FFT) (column (iii)). The ratio of column (ii) to column (iii) shows the magnitude of the improvement shown by the FFT. If each operation took 0.01 second then, for the series of length N = 8096, then the least-squares method would take 7 days, 18 h and 26 min. The FFT would accomplish the same result in 17min 45
s
.
Table 6.7 Correlation matrix, eigenvalues and eigenvectors of the combined 1984 and 1993 Alexandria images. The first six bands are TM bands for 1984. Bands 7–12 are the six TM bands for 1993. See Figure 6.41 for the first six principal component images.
Table 6.8 Canonical correlations and column eigenvectors (weights) for Alexandria 1984 TM and 1993 ETM+images. See text for discussion. Figures 6.43 and 6.44 show the images corresponding to these weights.
Table 6.9 Summary statistics for the data fusion example. The mean, standard deviation and entropy of the resampled multispectral image (RGB resampled) and for the four fusion methods (Gram-Schmidt, principal components, hue-saturation-intensity and wavelet) are shown. See text for elaboration.
Table 6.10 Columns show the correlation between the four fusion methods and the red, green and blue bands ofthe resampled multispectral false colour image.
7 Filtering Techniques
Table 7.1 Relationship between discrete values (f) along a scan line and the first and second differences (Δ(f), Δ
2
(f))). The first difference (row2\) indicates the rate of change of the values off shown in row 1. The second difference (row3) gives the po ints at which the rate of change itselfalters. The first difference is computed from Δ(f) = f
i
– f
i
–i
, and the second derivative is found from Δ
2
(f) = Δ(Δ(f)) = f
i+1
+ f
i−1
– 2f
i
.
Table 7.2 (a) Weight matrix for the Laplacian operator. (b) These weights subtract the output from the Laplacian operator from the value of the central pixel in the window.
8 Classification
Example 8.1 Table 1 ISODATA parameters and their effects.
Example 8.1 Table 2 Summary of the output from the ISODATA unsupervised classification.
Table 8.1 Variance–covariance matrices for four Landsat MSS bands obtained from random sample (upper figure) and contiguous sample (in parentheses) drawn from same data.
Example 8.2 Table 1 Percentage accuracy and corresponding kappa values for the classified images shown in Example 8.2 Figures 3–6.
Table 8.2 Columns C1 and C2 show the reflectance spectra for two pure types. Column M shows a 60: 40 ratio mixture of C1 and C2. See text for discussion.
Table 8.3 Example data and derived grey-tone spatial dependency matrices. (a) Test dataset. (b-e) Grey-tone spatial dependency matrices for angles of 0, 45, 90 and 135°, respectively.
Table 8.4 Confusion or error matrix for six classes. The row labels are those given by an operator using ground reference data. The column labels are those generated by the classification procedure. See text for explanation. (i) Number of pixels in class from ground reference data. (ii) Estimated classification accuracy (percent). (iii) Class i pixels in reference data but not given label by classifier. (iv) Pixels given label i by classifier but not class i in reference data. The sum of the diagonal elements of the confusion matrix is 350, and the overall accuracy is therefore (350/410) x 100= 85.4%.
9 Advanced Topics
Table 9.1 Bands 1–32 of the DAIS 7915 Imaging Spectrometer. The table shows the centre wavelength of each band together with the full width half maximum (FWHM) in nanometres (nm). The FWHM is related to the width of the band. See Figure 9.10.
Table 9.2 Summary of Hymap imaging spectrometer wavebands, bandwidths and sampling intervals.
Table 9.3 Matrices and vectors used in Savitzky-Golay example. (a) The design matrix, A. (b) Matrix product A'A. (c) Inverse matrix (A'A)
−
1
and (d) the matrix product (A'A)
−
1
A'.
Table 9.4 Two-dimensional moving window. The cell values are referenced by the × and y coordinates in the usual way.
10 Environmental Geographical Information Systems: A Remote Sensing Perspective
Table 10.1 Data structure used to store coordinate and topological data for polygon 1 in Figure 10.3.
Table 10.2 Weights and scores for thematic layers and their classes.
Table 10.3 Satellite dataset characteristics.
Table 10.4 Median backscatter (DN) values of Radarsat-1 and PALSAR data for each of the five classes with corresponding roughness/grain size as observed in the field.
Table 10.5 Spatial correlation ofhybrid classes (ETM+/Radarsat-1 and ETM+/PALSAR) with ma in underlying lithological un its and mean slope values.
Table 10.6 Predominant rock composition (end members) within each class produced by unsupervised classification of the hybrid image.