Remote Sensing Digital Image Analysis - Toc
Remote Sensing Digital Image AnalysisAn IntroductionBearbeitet vonJohn A. Richards1. Auflage 2012. Buch. xix, 494 S. HardcoverISBN 978 3 642 30061 5Format (B x L): 15,5 x 23,5 cmGewicht: 931 gWeitere Fachgebiete EDV, Informatik Informationsverarbeitung BildsignalverarbeitungZu Leseprobeschnell und portofrei erhältlich beiDie Online-Fachbuchhandlung beck-shop.de ist spezialisiert auf Fachbücher, insbesondere Recht, Steuern und Wirtschaft.Im Sortiment finden Sie alle Medien (Bücher, Zeitschriften, CDs, eBooks, etc.) aller Verlage. Ergänzt wird das Programmdurch Services wie Neuerscheinungsdienst oder Zusammenstellungen von Büchern zu Sonderpreisen. Der Shop führt mehrals 8 Millionen Produkte.
0.31323337Sources and Characteristics of Remote Sensing Image Data.1.1Energy Sources and Wavelength Ranges . . . . . . . . . . .1.2Primary Data Characteristics . . . . . . . . . . . . . . . . . . .1.3Remote Sensing Platforms . . . . . . . . . . . . . . . . . . . . .1.4What Earth Surface Properties are Measured? . . . . . . .1.4.1 Sensing in the Visible and ReflectedInfrared Ranges . . . . . . . . . . . . . . . . . . . . . .1.4.2 Sensing in the Thermal Infrared Range . . . . . .1.4.3 Sensing in the Microwave Range . . . . . . . . . .1.5Spatial Data Sources in General and GeographicInformation Systems . . . . . . . . . . . . . . . . . . . . . . . . .1.6Scale in Digital Image Data . . . . . . . . . . . . . . . . . . . .1.7Digital Earth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.8How This Book is Arranged . . . . . . . . . . . . . . . . . . .1.9Bibliography on Sources and Characteristicsof Remote Sensing Image Data . . . . . . . . . . . . . . . . .1.10Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Correcting and Registering Images . . . . . . . . . . . . . . . . . .2.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.2Sources of Radiometric Distortion . . . . . . . . . . . . . .2.3Instrumentation Errors . . . . . . . . . . . . . . . . . . . . . . .2.3.1 Sources of Distortion. . . . . . . . . . . . . . . . . .2.3.2 Correcting Instrumentation Errors . . . . . . . . .2.4Effect of the Solar Radiation Curve and theAtmosphere on Radiometry . . . . . . . . . . . . . . . . . . .2.5Compensating for the Solar Radiation Curve . . . . . . .2.6Influence of the Atmosphere . . . . . . . . . . . . . . . . . .2.7Effect of the Atmosphere on Remote Sensing Imagery.ix
82.192.202.21Correcting Atmospheric Effects in BroadWaveband Systems . . . . . . . . . . . . . . . . . . . . . . .Correcting Atmospheric Effects in NarrowWaveband Systems . . . . . . . . . . . . . . . . . . . . . . .Empirical, Data Driven Methods forAtmospheric Correction . . . . . . . . . . . . . . . . . . . .2.10.1 Haze Removal by Dark Subtraction . . . . .2.10.2 The Flat Field Method. . . . . . . . . . . . . . .2.10.3 The Empirical Line Method . . . . . . . . . . .2.10.4 Log Residuals . . . . . . . . . . . . . . . . . . . .Sources of Geometric Distortion . . . . . . . . . . . . . .The Effect of Earth Rotation . . . . . . . . . . . . . . . .The Effect of Variations in Platform Altitude,Attitude and Velocity . . . . . . . . . . . . . . . . . . . . .The Effect of Sensor Field of View:Panoramic Distortion. . . . . . . . . . . . . . . . . . . . . .The Effect of Earth Curvature . . . . . . . . . . . . . . .Geometric Distortion Caused by InstrumentationCharacteristics . . . . . . . . . . . . . . . . . . . . . . . . . .2.16.1 Sensor Scan Nonlinearities. . . . . . . . . . . .2.16.2 Finite Scan Time Distortion . . . . . . . . . . .2.16.3 Aspect Ratio Distortion . . . . . . . . . . . . . .Correction of Geometric Distortion . . . . . . . . . . . .Use of Mapping Functions for Image Correction . .2.18.1 Mapping Polynomials and the Useof Ground Control Points. . . . . . . . . . . . .2.18.2 Building a Geometrically Correct Image . .2.18.3 Resampling and the Need for Interpolation2.18.4 The Choice of Control Points . . . . . . . . . .2.18.5 Example of Registration to a Map Grid . . .Mathematical Representation and Correctionof Geometric Distortion . . . . . . . . . . . . . . . . . . . .2.19.1 Aspect Ratio Correction . . . . . . . . . . . . .2.19.2 Earth Rotation Skew Correction . . . . . . . .2.19.3 Image Orientation to North–South . . . . . .2.19.4 Correcting Panoramic Effects . . . . . . . . . .2.19.5 Combining the Corrections . . . . . . . . . . .Image to Image Registration . . . . . . . . . . . . . . . .2.20.1 Refining the Localisation of Control Points2.20.2 Example of Image to Image Registration . .Other Image Geometry Operations . . . . . . . . . . . .2.21.1 Image Rotation . . . . . . . . . . . . . . . . . . . .2.21.2 Scale Changing and Zooming. . . . . . . . . 162.646465666666676769717172.
Contents2.222.2334xiBibliography on Correcting and Registering Images . . . . . . .Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Interpreting Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.2Photointerpretation . . . . . . . . . . . . . . . . . . . . . . . . . . .3.2.1 Forms of Imagery for Photointerpretation . . . . .3.2.2 Computer Enhancement of Imageryfor Photointerpretation. . . . . . . . . . . . . . . . . . .3.3Quantitative Analysis: From Data to Labels . . . . . . . . . .3.4Comparing Quantitative Analysis and Photointerpretation3.5The Fundamentals of Quantitative Analysis . . . . . . . . . .3.5.1 Pixel Vectors and Spectral Space . . . . . . . . . . .3.5.2 Linear Classifiers . . . . . . . . . . . . . . . . . . . . . .3.5.3 Statistical Classifiers . . . . . . . . . . . . . . . . . . . .3.6Sub-Classes and Spectral Classes . . . . . . . . . . . . . . . . .3.7Unsupervised Classification . . . . . . . . . . . . . . . . . . . . .3.8Bibliography on Interpreting Images . . . . . . . . . . . . . . .3.9Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Radiometric Enhancement of Images . . . . . . . . . . . . . . .4.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4.1.1 Point Operations and Look Up Tables . . . . .4.1.2 Scalar and Vector Images . . . . . . . . . . . . .4.2The Image Histogram . . . . . . . . . . . . . . . . . . . . . .4.3Contrast Modification . . . . . . . . . . . . . . . . . . . . . .4.3.1 Histogram Modification Rule . . . . . . . . . . .4.3.2 Linear Contrast Modification . . . . . . . . . . .4.3.3 Saturating Linear Contrast Enhancement . . .4.3.4 Automatic Contrast Enhancement . . . . . . . .4.3.5 Logarithmic and Exponential ContrastEnhancement . . . . . . . . . . . . . . . . . . . . . .4.3.6 Piecewise Linear Contrast Modification. . . .4.4Histogram Equalisation . . . . . . . . . . . . . . . . . . . . .4.4.1 Use of the Cumulative Histogram . . . . . . . .4.4.2 Anomalies in Histogram Equalisation . . . . .4.5Histogram Matching . . . . . . . . . . . . . . . . . . . . . . .4.5.1 Principle . . . . . . . . . . . . . . . . . . . . . . . . .4.5.2 Image to Image Contrast Matching . . . . . . .4.5.3 Matching to a Mathematical Reference . . . .4.6Density Slicing . . . . . . . . . . . . . . . . . . . . . . . . . . .4.6.1 Black and White Density Slicing . . . . . . . .4.6.2 Colour Density Slicing and 115115118118119
xiiContents4.74.856Bibliography on Radiometric Enhancement of Images. . . . . .Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Geometric Processing and Enhancement: ImageDomain Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.2Neighbourhood Operations in Image Filtering . . . . . . . . .5.3Image Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.3.1 Mean Value Smoothing . . . . . . . . . . . . . . . . . . .5.3.2 Median Filtering . . . . . . . . . . . . . . . . . . . . . . . .5.3.3 Modal Filtering. . . . . . . . . . . . . . . . . . . . . . . . .5.4Sharpening and Edge Detection . . . . . . . . . . . . . . . . . . .5.4.1 Spatial Gradient Methods. . . . . . . . . . . . . . . . . .5.4.1.1The Roberts Operator . . . . . . . . . . . . .5.4.1.2The Sobel Operator . . . . . . . . . . . . . .5.4.1.3The Prewitt Operator . . . . . . . . . . . . .5.4.1.4The Laplacian Operator . . . . . . . . . . .5.4.2 Subtractive Smoothing (Unsharp Masking) . . . . .5.5Edge Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.6Line and Spot Detection . . . . . . . . . . . . . . . . . . . . . . . .5.7Thinning and Linking . . . . . . . . . . . . . . . . . . . . . . . . . .5.8Geometric Processing as a Convolution Operation . . . . . .5.9Image Domain Techniques Compared with Usingthe Fourier Transform . . . . . . . . . . . . . . . . . . . . . . . . . .5.10Geometric Properties of Images . . . . . . . . . . . . . . . . . . .5.10.1 Measuring Geometric Properties . . . . . . . . . . . . .5.10.2 Describing Texture . . . . . . . . . . . . . . . . . . . . . .5.11Morphological Analysis . . . . . . . . . . . . . . . . . . . . . . . . .5.11.1 Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.11.2 Dilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.11.3 Opening and Closing. . . . . . . . . . . . . . . . . . . . .5.11.4 Boundary Extraction . . . . . . . . . . . . . . . . . . . . .5.11.5 Other Morphological Operations . . . . . . . . . . . . .5.12Shape Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . .5.13Bibliography on Geometric Processing and Enhancement:Image Domain Techniques. . . . . . . . . . . . . . . . . . . . . . .5.14Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Spectral Domain Image Transforms . . . . . . . . . . . . . . . . .6.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6.2Image Arithmetic and Vegetation Indices . . . . . . . . .6.3The Principal Components Transformation . . . . . . . .6.3.1 The Mean Vector and The Covariance Matrix6.3.2 A Zero Correlation, Rotational Transform . . .6.3.3 The Effect of an Origin Shift . . . . . . . . . . . 158.161161162163164167173.
le and Some Practical Considerations . . .Application of Principal Componentsin Image Enhancement and Display . . . . . . . . .6.3.6 The Taylor Method of Contrast Enhancement . .6.3.7 Use of Principal Components for ImageCompression . . . . . . . . . . . . . . . . . . . . . . . . .6.3.8 The Principal Components Transform in ChangeDetection Applications . . . . . . . . . . . . . . . . . .6.3.9 Use of Principal Componentsfor Feature Reduction . . . . . . . . . . . . . . . . . . .The Noise Adjusted Principal Components Transform. . .The Kauth–Thomas Tasseled Cap Transform . . . . . . . . .The Kernel Principal Components Transformation . . . . .HSI Image Display . . . . . . . . . . . . . . . . . . . . . . . . . . .Pan Sharpening. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Bibliography on Spectral Domain Image Transforms . . .Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Spatial Domain Image Transforms . . . . . . . . . . . . . . . . . . .7.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7.2Special Functions . . . . . . . . . . . . . . . . . . . . . . . . . . .7.2.1 The Complex Exponential Function . . . . . . . .7.2.2 The Impulse or Delta Function . . . . . . . . . . . .7.2.3 The Heaviside Step Function . . . . . . . . . . . . .7.3The Fourier Series . . . . . . . . . . . . . . . . . . . . . . . . . .7.4The Fourier Transform . . . . . . . . . . . . . . . . . . . . . . .7.5The Discrete Fourier Transform . . . . . . . . . . . . . . . . .7.5.1 Properties of the Discrete Fourier Transform . .7.5.2 Computing the Discrete Fourier Transform . . .7.6Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7.6.1 The Convolution Integral . . . . . . . . . . . . . . . .7.6.2 Convolution with an Impulse . . . . . . . . . . . . .7.6.3 The Convolution Theorem . . . . . . . . . . . . . . .7.6.4 Discrete Convolution. . . . . . . . . . . . . . . . . . .7.7Sampling Theory . . . . . . . . . . . . . . . . . . . . . . . . . . .7.8The Discrete Fourier Transform of an Image . . . . . . . .7.8.1 The Transformation Equations . . . . . . . . . . . .7.8.2 Evaluating the Fourier Transform of an Image .7.8.3 The Concept of Spatial Frequency . . . . . . . . .7.8.4 Displaying the DFT of an Image . . . . . . . . . .7.9Image Processing Using the Fourier Transform . . . . . .7.10Convolution in two Dimensions . . . . . . . . . . . . . . . . .7.11Other Fourier Transforms . . . . . . . . . . . . . . . . . . . . .7.12Leakage and Window Functions . . . . . . . . . . . . . . . . 21222223223224226227227
xivContents7.137.147.157.167.178The Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . . . .7.13.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . .7.13.2 Orthogonal Functions and Inner Products . . . . . .7.13.3 Wavelets as Basis Functions . . . . . . . . . . . . . . .7.13.4 Dyadic Wavelets with Compact Support . . . . . . .7.13.5 Choosing the Wavelets . . . . . . . . . . . . . . . . . . .7.13.6 Filter Banks . . . . . . . . . . . . . . . . . . . . . . . . . . .7.13.6.1 Sub Band Filtering, and Downsampling .7.13.6.2 Reconstruction from the Wavelets,and Upsampling . . . . . . . . . . . . . . . . .7.13.6.3 Relationship Between the Lowand High Pass Filters . . . . . . . . . . . . .7.13.7 Choice of Wavelets. . . . . . . . . . . . . . . . . . . . . .The Wavelet Transform of an Image. . . . . . . . . . . . . . . .Applications of the Wavelet Transform in RemoteSensing Image Analysis. . . . . . . . . . . . . . . . . . . . . . . . .Bibliography on Spatial Domain Image Transforms . . . . .Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Supervised Classification Techniques. . . . . . . . . . . . . . . . . . . .8.1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8.2The Essential Steps in Supervised Classification. . . . . . . .8.3Maximum Likelihood Classification . . . . . . . . . . . . . . . .8.3.1 Bayes’ Classification . . . . . . . . . . . . . . . . . . . . .8.3.2 The Maximum Likelihood Decision Rule . . . . . .8.3.3 Multivariate Normal Class Models . . . . . . . . . . .8.3.4 Decision Surfaces . . . . . . . . . . . . . . . . . . . . . . .8.3.5 Thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . .8.3.6 Number of Training Pixels Required . . . . . . . . . .8.3.7 The Hughes Phenomenon and the Curseof Dimensionality . . . . . . . . . . . . . . . . . . . . . . .8.3.8 An Example . . . . . . . . . . . . . . . . . . . . . . . . . . .8.4Gaussian Mixture Models . . . . . . . . . . . . . . . . . . . . . . .8.5Minimum Distance Classification . . . . . . . . . . . . . . . . . .8.5.1 The Case of Limited Training Data. . . . . . . . . . .8.5.2 The Discriminant Function. . . . . . . . . . . . . . . . .8.5.3 Decision Surfaces for the Minimum DistanceClassifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8.5.4 Thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . .8.5.5 Degeneration of Maximum Likelihoodto Minimum Distance Classification . . . . . . . . . .8.5.6 Classification Time Comparison of the MaximumLikelihood and Minimum Distance Rules . . . . . .8.6Parallelepiped Classification. . . . . . . . . . . . . . . . . . . . . 267268.268.269269
.188.198.208.218.229xvMahalanobis Classification. . . . . . . . . . . . . . . . . . . . . . .Non-Parametric Classification . . . . . . . . . . . . . . . . . . . .Table Look Up Classification . . . . . . . . . . . . . . . . . . . . .kNN (Nearest Neighbour) Classification . . . . . . . . . . . . .The Spectral Angle Mapper . . . . . . . . . . . . . . . . . . . . . .Non-Parametric Classification from a Geometric Basis . . .8.12.1 The Concept of a Weight Vector . . . . . . . . . . . .8.12.2 Testing Class Membership . . . . . . . . . . . . . . . . .Training a Linear Classifier . . . . . . . . . . . . . . . . . . . . . .The Support Vector Machine: Linearly Separable Classes .The Support Vector Machine: Overlapping Classes. . . . . .The Support Vector Machine: Nonlinearly Separable Dataand Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Multi-Category Classification with Binary Classifiers . . . .Committees of Classifiers . . . . . . . . . . . . . . . . . . . . . . .8.18.1 Bagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8.18.2 Boosting and AdaBoost . . . . . . . . . . . . . . . . . . .Networks of Classifiers: The Neural Network . . . . . . . . .8.19.1 The Processing Element . . . . . . . . . . . . . . . . . .8.19.2 Training the Neural Network—Backpropagation. .8.19.3 Choosing the Network Parameters . . . . . . . . . . .8.19.4 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Context Classification . . . . . . . . . . . . . . . . . . . . . . . . . .8.20.1 The Concept of Spatial Context . . . . . . . . . . . . .8.20.2 Context Classification by Image Pre-processing . .8.20.3 Post Classification Filtering . . . . . . . . . . . . . . . .8.20.4 Probabilistic Relaxation Labelling. . . . . . . . . . . .8.20.4.1 The Algorithm . . . . . . . . . . . . . . . . . .8.20.4.2 The Neighbourhood Function. . . . . . . .8.20.4.3 Determining the CompatibilityCoefficients . . . . . . . . . . . . . . . . . . . .8.20.4.4 Stopping the Process. . . . . . . . . . . . . .8.20.4.5 Examples . . . . . . . . . . . . . . . . . . . . .8.20.5 Handling Spatial Context by MarkovRandom Fields . . . . . . . . . . . . . . . . . . . . . . . . .Bibliography on Supervised Classification Techniques . . .Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Clustering and Unsupervised Classification . . .9.1How Clustering is Used. . . . . . . . . . . . .9.2Similarity Metrics and Clustering Criteria9.3k Means Clustering . . . . . . . . . . . . . . . .9.3.1 The k Means Algorithm. . . . . . .9.4Isodata Clustering . . . . . . . . . . . . . . . . 312315.319319320322322323.
9.4.1 Merging and Deleting Clusters . . . . . . . . . . . .9.4.2 Splitting Elongated Clusters . . . .
Remote Sensing Digital Image Analysis An Introduction Bearbeitet von John A. Richards 1. Auflage 2012. Buch. xix, 494 S. Hardcover ISBN 978 3 642 30061 5 Format (B x L): 15,5 x 23,5 cm Gewicht: 931 g Weitere Fachgebiete EDV, Informatik Informationsverarbeitung Bildsignalverarbeitung Zu Leseprobe schnell und portofrei erhältlich bei Die Online-Fachbuchhandlung beck-shop.de ist .
PRINCIPLES OF REMOTE SENSING Shefali Aggarwal Photogrammetry and Remote Sensing Division Indian Institute of Remote Sensing, Dehra Dun Abstract : Remote sensing is a technique to observe the earth surface or the atmosphere from out of space using satellites (space borne) or from the air using aircrafts (airborne). Remote sensing uses a part or several parts of the electromagnetic spectrum. It .
Scope of remote sensing Remote sensing: science or art? The remote sensing process Applications of remote sensing Information flow in remote sensing The EMRreflected, emitted, or back-scattered from an object or geographic area is used as a surrogatefor the actual property under investigation.
Remote Sensing 15.1 REMOTE SENSING Remote sensing is the science of gathering information from a location that is distant from the data source. Image analysis is the science of interpreting specific criteria from a remotely sensed image. An individual may visually, or with the assistance of computer enhancement, extract information from an image, whether it is furnished in the form of an .
Chapter 3 Introduction to Remote Sensing and Image Processing 17 Introduction to Remote Sensing and Image Processing Of all the various data sources used in GIS, one of the most important is undoubtedly that provided by remote sensing. Through the use of satellites, we now have a continuing program of data acquisition for the entire world with time frames ranging from a couple of weeks to a .
Proximity Sensor Sensing object Reset distance Sensing distance Hysteresis OFF ON Output Proximity Sensor Sensing object Within range Outside of range ON t 1 t 2 OFF Proximity Sensor Sensing object Sensing area Output (Sensing distance) Standard sensing object 1 2 f 1 Non-metal M M 2M t 1 t 2 t 3 Proximity Sensor Output t 1 t 2 Sensing .
vi. supplemental remote sensing information vi-1 a. what remote sensing can do vi-1 b. new image types vi-1 c. image interpretation vi-1 d. general remote sensing terminology vl-3 e. aerial photography: types and exploitation vl-5 f. technology transfer vl-6 g. recommendations for future editions vl-7 h. acronyms vi-8 i. bibliography. vi-10 2 .
Jul 28, 2014 · imagery analysis are forms of remote sensing. Remote sensing, a term which refers to the remote viewing of the surrounding world, including all forms of photography, video and other forms of visualization (Parcak 2012) can be used to view live societies. Satellite remote sensing allows
Digital Image Fundamentals Titipong Keawlek Department of Radiological Technology Naresuan University Digital Image Structure and Characteristics Image Types Analog Images Digital Images Digital Image Structure Pixels Pixel Bit Depth Digital Image Detail Pixel Size Matrix size Image size (Field of view) The imaging modalities Image Compression .
