John wiley sons mather p computer processing of remotely sensed images an introduction (3rd ed)(isbn 0470849185)(2004)

Computer Processing of Remotely-Sensed Images Computer Processing of Remotely-Sensed Images An Introduction Third Edition Paul M Mather The University of Nottingham CD-ROM exercises contributed by Magaly Koch, Boston University Copyright C 2004 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 E-mail (for orders and customer service enquiries): cs-books@wiley.co.uk Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or e-mailed to permreq@wiley.co.uk, or faxed to (+44) 1243 770620 This publication is designed to provide accurate and authoritative information in regard to the subject matter covered It is sold on the understanding that the Publisher is not engaged in rendering professional services If professional advice or other expert assistance is required, the services of a competent professional should be sought Other Wiley Editorial Offices John Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USA Jossey-Bass, 989 Market Street, San Francisco, CA 94103-1741, USA Wiley-VCH Verlag GmbH, Boschstr 12, D-69469 Weinheim, Germany John Wiley & Sons Australia Ltd, 33 Park Road, Milton, Queensland 4064, Australia John Wiley & Sons (Asia) Pte Ltd, Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809 John Wiley & Sons Canada Ltd, 22 Worcester Road, Etobicoke, Ontario, Canada M9W 1L1 Wiley also publishes its books in a variety of electronic formats Some content that appears in print may not be available in electronic books Library of Congress Cataloging-in-Publication Data Mather, Paul M Computer processing of remotely-sensed images: an introduction / Paul M Mather.–3rd ed p cm Includes bibliographical references and index ISBN 0-470-84918-5 (cloth : alk paper) – ISBN 0-470-84919-3 (pbk.: alk paper) Remote sensing–Data processing I Title G70.4.M38 2004 621.36 78–dc22 2004005079 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 0-470-84918-5 (HB) ISBN 0-470-84919-3 (PB) Typeset in 9/11pt Times by TechBooks, New Delhi, India Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham, Wiltshire This book is printed on acid-free paper responsibly manufactured from sustainable forestry in which at least two trees are planted for each one used for paper production ‘I hope that posterity will judge me kindly, not only as to the things which I have explained but also as to those which I have intentionally omitted so as to leave to others the pleasure of discovery.’ Ren´e Descartes ‘I am none the wiser, but I am much better informed.’ Queen Victoria Contents Preface to the First Edition Preface to the Second Edition Preface to the Third Edition List of Examples Remote sensing: basic principles 1.1 Introduction 1.2 Electromagnetic radiation and its properties 1.2.1 Terminology 1.2.2 Nature of electromagnetic radiation 1.2.3 The electromagnetic spectrum 1.2.4 Sources of electromagnetic radiation 1.2.5 Interactions with the Earth’s atmosphere 1.3 Interaction with Earth surface materials 1.3.1 Introduction 1.3.2 Spectral reflectance of Earth-surface materials 1.4 Summary Remote sensing platforms and sensors 2.1 Introduction 2.2 Characteristics of imaging remote sensing instruments 2.2.1 Spatial resolution 2.2.2 Spectral resolution 2.2.3 Radiometric resolution 2.3 Optical, near-infrared and thermal imaging sensors 2.3.1 Along-Track Scanning Radiometer (ATSR) 2.3.2 Advanced Very High Resolution Radiometer (AVHRR) 2.3.3 MODIS (MODerate Resolution Imaging Spectrometer) 2.3.4 Ocean-observing instruments 2.3.5 IRS-1 LISS 2.3.6 Landsat instruments 2.3.7 2.3.8 SPOT sensors Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 2.3.9 High-resolution commercial and micro-satellite systems 2.4 Microwave imaging sensors 2.4.1 ERS SAR 2.4.2 RADARSAT 2.5 Summary xi xiii xvi xviii 1 3 5 12 15 17 17 19 24 25 25 26 26 30 33 35 35 36 36 38 40 40 Hardware and software aspects of digital image processing 3.1 Introduction 3.2 Properties of digital remote sensing data 3.2.1 Digital data 3.2.2 Data formats 3.2.3 System processing 3.3 MIPS software 3.3.1 Installing MIPS 3.3.2 Using MIPS 3.3.3 Summary of MIPS functions 3.4 Summary Pre-processing of remotely-sensed data 4.1 Introduction 4.2 Cosmetic operations 4.2.1 Missing scan lines 4.2.2 De-striping methods 4.3 Geometric correction and registration 4.3.1 Orbital geometry model 4.3.2 Transformation based on ground control points 4.3.3 Resampling procedures 4.3.4 Image registration 4.3.5 Other geometric correction methods 4.4 Atmospheric correction 4.4.1 Background 4.4.2 Image-based methods 43 48 50 52 55 55 57 59 59 59 59 64 70 71 71 72 74 78 80 80 81 82 83 87 88 91 104 107 107 108 108 109 viii Contents 4.5 4.6 4.7 4.8 4.4.3 Radiative transfer model 4.4.4 Empirical line method Illumination and view angle effects Sensor calibration Terrain effects Summary 110 111 111 112 116 118 Image enhancement techniques 5.1 Introduction 5.2 Human visual system 5.3 Contrast enhancement 5.3.1 Linear contrast stretch 5.3.2 Histogram equalisation 5.3.3 Gaussian stretch 5.4 Pseudocolour enhancement 5.4.1 Density slicing 5.4.2 Pseudocolour transform 5.5 Summary 120 120 121 122 123 127 130 132 133 134 135 Image transforms 6.1 Introduction 6.2 Arithmetic operations 6.2.1 Image addition 6.2.2 Image subtraction 6.2.3 Image multiplication 6.2.4 Image division and vegetation indices 6.3 Empirically based image transforms 6.3.1 Perpendicular Vegetation Index 6.3.2 Tasselled Cap (Kauth-Thomas) transformation 6.4 Principal components analysis 6.4.1 Standard principal components analysis 6.4.2 Noise-adjusted principal components analysis 6.4.3 Decorrelation stretch 6.5 Hue, Saturation and Intensity HSI Transform 6.6 The Discrete Fourier Transform 6.6.1 Introduction 6.6.2 Two dimensional DFT 6.6.3 Applications of the DFT 6.7 The Discrete Wavelet Transform 6.7.1 Introduction 6.7.2 The one-dimensional discrete wavelet transform 6.7.3 Two-dimensional discrete wavelet transform 136 136 137 137 138 140 6.8 140 145 145 146 149 149 158 160 161 162 162 163 169 170 170 171 174 Summary Filtering techniques 7.1 Introduction 7.2 Spatial domain low-pass (smoothing) filters 7.2.1 Moving average filter 7.2.2 Median filter 7.2.3 Adaptive filters 7.3 Spatial domain high-pass (sharpening) filters 7.3.1 Image subtraction method 7.3.2 Derivative-based methods 7.4 Spatial domain edge detectors 7.5 Frequency domain filters 7.6 Summary Classification 8.1 Introduction 8.2 Geometrical basis of classification 8.3 Unsupervised classification 8.3.1 The k-means algorithm 8.3.2 ISODATA 8.3.3 A modified k-means algorithm 8.4 Supervised classification 8.4.1 Training samples 8.4.2 Statistical classifiers 8.4.3 Neural classifiers 8.5 Fuzzy classification and linear spectral unmixing 8.5.1 The linear mixture model 8.5.2 Fuzzy classifiers 8.6 Other approaches to image classification 8.7 Incorporation of non-spectral features 8.7.1 Texture 8.7.2 Use of external data 8.8 Contextual information 8.9 Feature selection 8.10 Classification accuracy 8.11 Summary Advanced topics 9.1 Introduction 9.2 SAR Interferometry 9.2.1 Basic principles 9.2.2 Interferometric processing 9.2.3 Problems in SAR interferometry 177 180 180 181 183 186 187 188 189 189 194 195 201 203 203 204 207 207 208 209 214 214 217 224 227 228 234 236 238 238 240 241 242 245 248 250 250 250 250 255 258 Contents ix 9.2.4 9.3 9.4 Applications of SAR interferometry Imaging spectrometry 9.3.1 Introduction 9.3.2 Processing imaging spectrometry data Lidar 9.4.1 Introduction 9.4.2 Lidar details 258 259 259 264 281 281 283 9.5 9.4.3 Lidar applications Summary 285 287 Appendix A: Using the CD-ROM Image Data Sets 289 References Index 292 319 CONTENTS OF CD-ROM MIPS image processing software (MS Windows) WWW pages containing links to 1000+ sites of interest Test images Four advanced examples, including datasets (contributed by Magaly Koch, Boston University, Boston, MA) References 317 Mapper Plus radiometric and geometric calibrations and corrections on landscape characterisation Remote Sensing of Environment, 78, 55–70 Wakabayeshi, H and Arai, K., 1996, A new method for SAR speckle noise reduction (CST filter) Canadian Journal of Remote Sensing, 22, 190–197 Walker, J.S., 1999, A Primer on Wavelets and their Scientific Applications 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California: IEEE Computer Society Press Wolfe, P.R and DeWitt, B.A., 2000, Elements of Photogrammetry with Applications to GIS (third edition) New York: McGraw-Hill Wong, F., Orth, R and Friedmann, D., 1981, The use of digital terrain models in the rectification of satelliteborne imagery Proceedings of the 15th International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan: Environmental Research Institute of Michigan (ERIM), 653–662 Woodcock, C and Harward, V.J., 1992, Nestedhierarchical scene models and image segmentation International Journal of Remote Sensing, 16, 3167–3187 Woodcock, C.E and Strahler, A.H., 1987, The factor of scale in remote sensing Remote Sensing of Environment, 21, 311–322 Woodcock, C.E., Strahler, A.H and Jupp, D.L.B., 1988a, The use of variograms in remote sensing I: Scene models and simulated images Remote Sensing of Environment, 25, 323–348 318 References Woodcock, C.E., Strahler, A.H and Jupp, D.L.B., 1988b, The use of variograms in remote sensing II: Real digital images Remote Sensing of Environment, 25, 349– 379 Woodham, R.J., 1989, Determining intrinsic surface reflectance in rugged terrain and changing illumination Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS’89), 10–14 July 1989, Vancouver, British Columbia, Canada New York: IEEE Press, volume 1, 1–5 Wooding, M.G., Zmuda, A.D and Griffiths, G.H., 1993, Crop discrimination using multi-temporal ERS-1 SAR data Proceedings of the Second ERS-1 Symposium on Space at the Service of our Environment, Hamburg, Germany ESA SP-361 Paris: European Space Agency, 51–56 Wu, H.-H.P and Schowengerdt, R.A., 1993, Improved fraction image estimation using image restoration IEEE Transactions on Geoscience and Remote Sensing, 31, 771–778 Xie, H., Pierce, L.E and Ulaby, F.T., 2002, SAR speckle reduction using wavelet denoising and Markov Random Fields IEEE Transactions on Geoscience and Remote Sensing, 40, 2196–2212 Xu, H., Dvorkin, J and Nur, A., 2001, 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Young, T.L and Kaufman, Y.J., 1986, Non-Lambertian effects on remote sensing of surface reflectance and vegetation index IEEE Transactions on Geoscience and Remote Sensing, GE-24, 699–707 Yuan, D and Elvidge, C.D., 1996, Comparison of radiometric normalization techniques ISPRS Journal of Photogrammetry and Remote Sensing, 51, 117–126 Zebker, H.A and Goldstein, R.M., 1986 Topographic mapping from interferometric synthetic aperture radar observations Journal of Geophysical Research, 91, 4993–4999 Zebker, H.A., Werner, C.L., Rosen, P.A and Hensley, S., 1994 Accuracy of topographic maps derived from ERS-1 interferometric radar IEEE Transactions on Geoscience and Remote Sensing, 32, 823–836 Zervakis, M.E., Sundararajan, V and Parhi, K.K., 2001, Vector processing of wavelet coefficients for robust image denoising Image and Vision Computing, 19, 435–450 Zhang, Y., 1999, A new merging method and its spectral an spatial effects International Journal of Remote Sensing, 20, 2003–2014 Zhou, J and Civco, D.L., 1996, Using genetic learning neural networks for spatial decision making in GIS Photogrammetric Engineering and Remote Sensing, 62, 1287–1295 Zhou, J., Civco, D.L and Silander, J.A., 1999, A wavelet transform method to merge Landsat TM and SPOT data International Journal of Remote Sensing, 19, 743– 757 Zwally, H.J., Schutz, B., Abdalati, W., Abshire, J., Bentley, C., Brenner, A., Bufton, J., Dezio, J., Hancock, D., Harding, D., Herring, T., Minster, B., Quinn, K., Palm, S., Spinhirne, J and Thomas, R., 2002, ICESat’s laser measurements of polar ice, atmosphere, ocean, and land Journal of Geodynamics, 34, 405–445 Index 5S/6S 110 absorption band 264 absorption, atmospheric 9–10, 15, 108 across-track stereo 49 Adaboost 237 adaptive filter 187–188 addition, of images 137–138 adjacency effect 108 Advanced Land Imager 43, 263 ASTER, see Advanced Spaceborne Thermal Emission and Reflection Radiometer Advanced Very High Resolution Radiometer, see AVHRR aerial photography ALI, see Advanced Land Imager aliasing 166, Figure 6.18 Along-Track Scanning Radiometer, see ATSR along-track stereo 45, 49 AlSat 50 amplitude spectrum 163, 164, 169 amplitude 7, Figure 1.8, 163, 168, 174 analogue image analytical chemistry 259 ANN, see Artificial Neural Network Artificial Neural Network 224, 228 –, advantages and disadvantages 226–227 –, feed-forward 224 –, learning rate 226 apparent reflectance 116 Aqua 43 Artemis 12 artificial neural network, see ANN ASAR 54, 254 Advanced Spaceborne Thermal Emission and Reflection Radiometer 45, 48–50, 66, Table 2.4, Example 2.6 ASTER spectral library 21 ASTER, quantisation 60 ASTER, sensor calibration 115–116, Table 4.6 Astronomical Unit 116 Atlas 46 atmosphere 15–17 atmospheric correction 108–112 –, dark object subtraction 109 –, empirical line 111 atmospheric effects 81 atmospheric path radiance 17 atmospheric transmittance 259, Figure 9.10 (a) atmospheric window 15, 17, 36, 259 ATREM 110 ATSR 35–36, Example attenuation 16 autocorrelation 34 autocorrelation, spatial 215 AVHRR 10, Figure 1.12, 35, 36, 46 AVIRIS 250, 262–263 azimuth angle, solar 18, 108 Bach, J.S 181 back-propagation rule 225 backwards elimination 244 bagging 237 Band Interleaved by Line format 64–65 Band SeQuential format 64–65 band pass filter 196 base two representation 61, 62 baseline, critical 253 –, interferometric 253, 255, 258 Bhattachatyya distance 243, 244 Bidirectional Reflectance Distribution Function, 18, 20, 108, 112 BIL, see Band Interleaved by Line format bilinear resampling 104 binary number 33 bits per pixel 33–34 bivariate regression 93 blackbody 13, Figure 1.16 boosting 237 BRDF, see Bidirectional Reflectance Distribution Function BSQ, see Band SeQuential format Butterworth filter 171, 196, 198 calibration coefficients 113 calibration, sensor 112–116 camera, digital CCD, see charge coupled device change, temporal 152 charge coupled device chemometric analysis 250 chirp function 173 chlorosis 21 classification, accuracy 214, 245–248 –, defined 203 –, fuzzy 227–228, 234–236 –, geometrical basis 204–207 –, supervised 203, 214–227 –, unsupervised 203, 207–214 –, hard 228 –, k-means 207–208, 209–210, 220 –, maximum likelihood 221–224 –, neural 224–227 –, non-parametric 214 –, parallelepiped 217, 218–220 –, parametric 214 Computer Processing of Remotely-Sensed Images: An Introduction, Third Edition Paul M Mather c 2004 John Wiley & Sons, Ltd ISBNs: 0-470-84918-5 (HB); 0-470-84919-3 (PB) 320 Index –, soft 228 –, statistical 214, 217–224 clustering 203, 221 Coastal Zone Colour Scanner, see CZCS coherence map 256, Figure 9.6, Figure 9.8 coherence, in interferometry 253, 254 coherent signal 250 colour space 121 commercial satellite 50 complex conjugate 254 complex multiplication 254 confusion matrix 245–246 connectionist model 224 context 204, 241 continuous wave lidar 281 continuum removal 279–281, Figures 9.21, 9.22 convex hull 279, Figure 9.21 correlation 149, 150, 155 cosine theta coefficient 233, 234 critical baseline 253 cubic convolution resampling 104 CW, see continuous wave lidar CZCS 32, 38 DAIS 7915 66, 250, 260–262, 263, Example 9.1, Example 9.2 DAIS 7915, wavebands Figure 9.10 (a), Table 9.1 dark object subtraction 109 data cube 262, Figure 9.11 data format 64–65 Daubechies wavelet 271, 275 decimation 172 decision tree 236 decorrelation stretch 160–161 Dempster-Shafter theory 236 dendrogram 211 denoising, reflectance spectrum 266, 270–271, Example 9.3 density slicing 133 derivative analysis 264–266, 274–275, derivative function, properties 264 derivative 137 –, calculation 264 –, defined 189 derivative-based filter 189 destriping 82, 83–86 difference, of images 138–140 differential SAR interferometry 254 diffuse reflection 18 Digital Elevation Model 44, 45, 49, 93, 107, 118, 140–141, 285, 287, Figure 2.12 DEM, see Digital Elevation Model Digital Surface Model 250, 251, 281, 285 DInSAR, see differential SAR interferometry DIODE 45 direct address table 85 directional filter 196, 198 Disaster Monitoring Constellation, see DMC Discrete Fourier Transform 137, 162–170, 181, Example 6.2 Discrete Wavelet Transform, Example 9.3 divergence 243 –, transformed 243 division, of images 140–145 DLR, see German Space Agency DMC 50 Doppler principle, 53 DORIS 45, 88 DSM, see Digital Surface Model dwell time 32 dyadic sampling 172 Earth–Sun distance 116 Earth Explorer 250 Earth Observing 43, 58 Earth rotation correction 89 edge detector 194–195 edge preserving filter 188 edge, defined 194 Effective Instantaneous Field of View, see EIFOV Effective Resolution Element, see ERE EIFOV 30 eigenvalue 151 eigenvector 151 Einstein, A electromagnetic energy, terminology 3–5 electromagnetic spectrum 5–12, Figure 1.7 elevation angle, solar 18, 108 Enhanced Thematic Mapper Plus, see Landsat, ETM+ entropy measure 34–35 environmental radiance 30, 108 Envisat 36 equalisation 63 equiprobability ellipse 221 ERE 29 ERS 55 Euclidean distance 205 evidential reasoning 236 exo-atmospheric solar irradiance Table 4.7 extinction 16 eye, sensitivity 8, Figure 1.9 false colour 63, Figure 3.7 far range 57 feature selection 242–245 feature, defined 203 feed-forward neural network 224 Fick’s Law 191 filter, adaptive 187–188 –, band-pass 196 –, derivative-based 189 –, directional 196 –, edge preserving 188 –, frequency domain 195–202 –, high-pass 171, 180 –, low-pass 171, 180 –, median 186–187 –, moving average 183–185, Example 7.2 –, transfer function 195 filtering 180 flat Earth correction 257 floodplain modelling 287 foreshortening, radar 56 forward selection 244 Fourier transform 137, 173 frequency domain 162, 181 frequency Full Width Half Maximum 259, 262, Figure 9.10 (b) function, mathematical 264 Index 321 fuzzy classication 227–228, 234–236 fuzzy c-means algorithm 235 FWHM, see Full Width Half Maximum gain, sensor 114 Gaussian contrast stretch 130–131 Gaussian distribution 130, Figure 5.11 GCP, see ground control point generalised delta function 225 genetic algorithm 236, 244 geocoding, SAR 107 geology, reflectance 21 geometric correction 45, 87–108 –, polynomial 91–103 georeference 81 georeferencing 254 Geoscience Laser Altimeter System 283 geostatistical methods 216, 228 GeoTIFF 66 German Space Agency 251, 260, Figure 9.3 glacier movement 286 GLAS, see Geoscience Laser Altimeter System GLCM 188, 238–239 Global Positioning System 87, 92, 254 GPS, see Global Positioning System Gram-Schmidt orthogonal polynomial 148 Grey Level Co-occurrence Matrix, see GLCM ground control point 88, 92 –, correlation method 95–100 –, spatial distribution 94 ground range 257 Hamlet 15 Hebbian learning 225 Herschel, Sir W Hertz, H hexcone 122 histogram equalisation 63, 127 histogram minimum, atmospheric correction 109 histogram, cumulative 128 HSI model 122, 137, Figure 5.6 HSI transform 161–162, 170 hue 122 Hue-Saturation-Intensity, see HSI Huffman coding 70 human visual system 121–122 Hymap 262, 263, 276 Hyperion 43, 250, 263 hyperspectral data cube 262, Figure 9.11 ICESat 250, 283 ideal filter 198 IFOV 28, Figure 2.2, 2.4 IKONOS 50–51, 107 illumination geometry 108 image registration 107, 254 image sharpening 188–194 image transform 136 image, analogue imaging spectrometry 137, 250, 259–281 inclination, orbit 26, Figure 2.1 INF file 72 infrared infrared, short wave infrared, thermal InSAR, see interferometric SAR Instantaneous Field of View, see IFOV integration time 32 intensity 122 interferogram Figure 9.7 interferometric SAR 250–259 –, applications 258 –, DEM quality 258 –, comparison with lidar 258 interferometry 162 –, repeat-pass 251, 253, 256 –, single-pass 251, 253, 256 irradiance, defined IRS-1 40 ISODATA classifier 208–209, Example 8.1 Izmit earthquake 256 Joule, J.P JPEG 68 Julian date 113, 116 kappa coefficient 246 Kauth-Thomas transform, see Tasselled Cap k-means classifier 207–208, 209–210, 220 Kohonen Self-Organising Map 227 La Mancha 260, Example 9.1 Lambert’s cosine law 19, Figure 1.20 Lambertian reflectance 18 –, correction 118 Landsat ETM+ 43, 260, Example 2.4 –, reading image data Example 3.1 –, sensor calibration 114 Landsat follow-on 43 Landsat MSS 40–41, Example 2.3 Landsat Multispectral Scanner, see Landsat MSS Landsat TM 9, 41–42 –, sensor calibration 113, Table 4.3, Table 4.4 Laplacian operator 191 Las Vegas Example 2.3 layover 56 Lee sigma filter 188 leverage 94 lidar bathymetry 286 lidar instrument, operation 283–285, Figure 9.25 lidar 281–287 –, defined 281 –, first return 281, Figure 9.24, Example 9.4 –, footprint 281, 283 –, last return 281, Figure 9.24, Example 9.4 light, speed of lightsat 46 lineament, defined 195 linear combination 136 linear contrast stretch 123, figures 5.6, 5.7, Example 5.1 linear mixture model 228–232 linear mixture model, constraints 231 LISS 40 lookup table 85, 123 LOWTRAN 110 LUT, see lookup table 322 Index MAD, see Median Absolute Deviation Mahalanobis distance 216, 223 majority voting 237 map, defined 87 masking, logical 140 maximum likelihood classifier 221–224, 227, 228 Median Absolute Deviation 271 median filter 186–187 metadata 66 microwave sensor 52–57 microwave, active 10 –, passive 10 Minnaert correction 118 MIPS INF file 72 MIPS 271, 275 MIPS, installing 71–7 MISR 19 missing scan line 82–83 mixed pixel 217, 228 Moderate Resolution Imaging Spectrometer, see MODIS MODIS 36–38, 38–40, 46, 275, Example 2.2 MODTRAN 110 modulation transfer function, see MTF modulation 29 MOMS-02 169 moving average filter 183–185, Example 7.2 moving window 184 MTF 30 Multi-angle Imaging Spectro-Radiometer, see MISR multiplication, of images 140 PCA 136, 149–158, 232, 242, Example 6.1 PCA, noise-adjusted 158–160 perceptron 225 per-field classification 237 period perpendicular vegetation index 145–146 phase difference 250, 251, Figure 9.1 phase unwrapping 254, 257, Figure 9.6, Figure 9.9 phase, defined 250 photoreceptor cell 121 pixel size 30 pixel, defined –, labelling 203 –, mixed 217, 228 Planck’s constant Planck’s Law 13 Pléades 46, 50 point spread function, see PSF polarimetric SAR, see SAR, polarimetric polarisation polynomial function 93–103 PRARE, see Precise Range and Range Rate Equipment Precise Range and Range Rate Equipment 253 Principal Components Analysis, see PCA prior probabilities, in ML classification 240 prior probability 223 probabilistic relaxation 242 probability 221 pseudocolour 63, 132–135, 170, Figure 3.7 PSF 28, 188, 193, Figures 2.3, 2.4 Pythagoras’s theorem 206 Nagao/Matsuyama filter 188, Figure 7.7 natural colour 63, Figure 3.7 NDVI 142–144, 274 near range 57 nearest neighbour resampling 104 neural classifier 224–227 neuron 224 Newton, Sir I Nonlinear Mapping 217, 232 Normal distribution 130, Figure 5.11 Normalised Difference Vegetation Index, see NDVI quadtree 70 Quickbird 51–52 Ocean Colour and Temperature Sensor, see OCTS oceanography 38–40 OCTS 38 offset, sensor 114 optical bands orbit, inclination 26, Figure 2.1 orbit, satellite 25–26, Figure 2.1 orbit, sun-synchronous 26, Figure 2.1 orbital geometry 88–91 ordination 217 Orion 46 ozone hole 15 pan sharpening 177 panoramic distortion 87 parallelepiped classifier 217, 218–220 path radiance 17 pattern recognition 203, 204 pattern, defined 203 radar 11 –, history 52 –, wavebands 11, Table 1.3 radar foreshortening 56 radar layover 56 radar shadow 56 RADARSAT 54, 55, 57, 254 radian 4, Figure 1.6 radiance 4, Figure 1.5 –, apparent 110 –, at-sensor 110 –, environmental 30 radiant emittence radiant energy radiant exitance radiant flux density, defined radiative transfer model 110 radiometric correction 81 radiometric resolution 33–35 RAL 35 raster data Figure 3.1 raster, defined 254 ratio, of images, 140–145 rational polynomials 107 Rayleigh criterion 29 Rayleigh scattering 16, 109 RBV 41 reclassify 213 Index 323 red edge 19, 271–276 –, Guyot/Baret method 275, Figure 9.19 reflectance –, apparent 116 registration, of images 107 remote sensing, defined remote sensing, history 25 repeat-pass interferometry, see interferometry, repeat-pass resampling 104–106 residual error, geometric correction 94–95 resolution, radiometric: see radiometric resolution resolution, spatial: see spatial resolution resolution, spectral: see spectral resolution Return Beam Vidicon, see RBV RGB colour cube, see RGB model RGB model 121, 137, Figure 5.5 ringing, in frequency domain 196–198 RMS error 230 Roberts Gradient 191, 194, 264, Figure 7.9 (b) root mean squared error 230 run length encoding 70 Rutherford-Appleton Laboratory 35 SAM, see Spectral Angle Mapper Sammon’s Nonlinear Mapping 217 SAR 11, 53 –, depression angle 54 –, incidence angle 54 –, look angle 54 –, polarimetric 254 –, polarisation 54 –, speckle 55 satellite laser ranging 253–254 saturation 122 Savitzky-Golay polynomial 75, 266–270 scale, spatial 180 scaling, pixel values 62 scanner, electro-mechanical 1, Figure 1.1 –, pushbroom 1, Figure 1.2, 32 Scanning Multichannel Microwave Radiometer, see SMMR ScanSAR 55, 254, Figure 9.5 scattering, relative 17 –, atmospheric 15, 108, Figure 1.4 –, diffuse 16 –, Rayleigh 16 Schrödinger, E Seasat 11 Sea-Viewing Wide Field of View Sensor, see SEAWiFS SeaWiFS 38 segmentation 237 Self-Organising Map 227 sensor calibration 112–116 shadow, radar 56 sharpening image 188–194 Shuttle Laser Altimeter 283 Shuttle Radar Topography Mission 251, 253, Figures 9.2 and 9.3 Side-looking Airborne Radar, see SLAR sigma filter 188 signal-to-noise ratio, see SNR single-look complex image 251, 254, 256 single-pass interferometry, see interferometry, single-pass singular value decomposition 231–232 singularity, in numerical analysis 94 SIR-C 254 skylight 16 SLA, see Shuttle Laser Altimeter slant range 56 SLAR 52 SLC, see single-look complex image SMMR 10 smoothing 181–188 SNR 137, 32, 34 Sobel filter 194, Figure 7.12 soil line 146 soil, reflectance 23–24 solar azimuth angle 18 solar constant 12–13 solar elevation angle 18 solar irradiance 15, Figure 1.17 solar zenith angle 18 SOM 227 spatial autocorrelation 215 spatial domain 162, 181 spatial resolution, 26–30 speckle 55, 83, 187, 188 Spectral Angle Mapper 232–234 spectral reflectance curve 18 spectral resolution 30–33 spectral signature 18 spectral unmixing, see linear mixture model specular reflection 18 speed of light SPOT follow-on programme 47 SPOT HRV, sensor calibration 115, Table 4.5 SPOT, Supermode 45, Example 2.5, Figure 2.11 –, High Resolution Geometric: see SPOT, HRG –, High Resolution Stereoscopic: see SPOT, HRS –, High Resolution Visible: see SPOT, HRV –, HRG 44, 45, Table 2.3 –, HRS 44, Table 2.3 –, HRV 43–44 –, stereo capability 44 –, Vegetation: see SPOT, VGT –, VGT 46 SRTM, see Shuttle Radar Topography Mission SSTL 50 stacked vectors 241 steepest descent minimisation 225 Stefan-Bolzman Law 13, 14 steradian 4, Figure 1.6 sub-band coding 172 subtraction 189 subtraction, of images 138–140 Sun, properties 12 sunglint 22 sun-synchronous 16 supervised classification 203, 214–227 Surrey Space Technology Ltd, see SSTL SWIR, see infrared, short wave Synthetic Aperture Radar interferometry, see interferometric SAR Synthetic Aperture Radar, see SAR system processing 70–71 324 Index tandem mode, ERS 253 tasselled cap 146–148, 149 TDRS 12, 36–37 temporal decorrelation 253, 256–257, 258 Terra 43, 48 terrain correction 116–118 texture 204, 238–240 Thematic Mapper, see Landsat TM Thetford Forest 276, Figure 9.20 TIFF 68 time-scale diagram 174 TopSat 46 Tracking and Data Relay system, see TDRS training sample 214–217 tri-stimulus theory, human vision 121 tutorial, Nicholas Short UK National Grid 87 Universal Threshold 270, 271 unsupervised classification 203, 207–214 UT, see Universal Threshold variance-covariance estimators, robust 216–217 variance-covariance matrix 150, 155 VCL, see Vegetation Canopy Lidar Vegetation Canopy Lidar 283, 285 vegetation index 136, 170, 271 –, soil-adjusted 144 vegetation, reflectance 19–21 view-angle correction 112 visible spectrum volume reflectance 22 water, reflectance 21–23 Watt, J wavelength 6, Figure 1.8 wavelet 266, 270–271 wavelet shrinkage 271 wavelet transform 137, 170–177, 188 wavelet, detail coefficients 172 Wien’s Displacement Law 14, 15, Figure 1.15 Windowed Fourier transform 170 Windows bitmap 123 Wittgenstein, L 203 WWW links zenith angle 18 zero crossing 274, 276 .. .Computer Processing of Remotely- Sensed Images Computer Processing of Remotely- Sensed Images An Introduction Third Edition Paul M Mather The University of Nottingham CD-ROM... Topographic and atmospheric interactions are described in sections 4.7 and 4.4, respectively Computer Processing of Remotely- Sensed Images: An Introduction, Third Edition Paul M Mather C 2004 John. .. subject of chapter The remaining five chapters cover particular topics within the general field of the processing of remotely- sensed data in the form of digital images, and their application to a range