Digital Image. Processing. Alpha. Science International Ltd. Oxford, U.K. S. K. Ghosh

Transcription

1 Digital Image Processing S. K. Ghosh Alpha Science International Ltd. Oxford, U.K.

2 Preface Acknowledgement List offigures List of Tables vii ix xix xxiii 1. Concept of Images Introduction Electromagnetic Energy Electromagnetic Spectrum and its Characteristics Utility of EM Radiation in Image Acquisition Image Processing Basic Image Processing Technique Image representation and modeling Image enhancement Image restoration Image analysis Image reconstruction Image data compression The Process of Imaging Introduction Passive Sensors Gamma-ray spectrometer Aerial camera Video camera Multi-spectral scanner Imaging spectrometer Thermal scanner Radiometer Active Sensors Laser scanner Radar altimeter Imaging radar Platforms Characteristics of Image 2.5

3 " xii Sampling Spatial resolution Sampling pattern Quantization Colour Fundamentals Colour Models The RGB model CMY model HSI model Conversion of colour from RGB to HSI Converting colours from HSI to RGB 2.15 Image File Format Storage Media File Formats Common Interchangeable Formats Bitmap (BMP) Tagged Image File Format (TIFF) The TIFF file structure TIFF data compression TIFF classes Graphic Interchange Format (GIF) Joint Photographic Expert Graphic (JPEG) Portable Network Graphics (PNG) File structure of PNG PNG file signature Chunk layout Chunks specifications Primary Chunks IHDR chunk PLTE chunk IDAT chunk IEND chunk Ancillary chunks bkgd chunk chrm chunk gama chunk hist chunk phys chunk sbit chunk text chunk time chunk trns chunk ztxt chunk Summary of standard chunks Shape File The main file header 3.24

4 xiii Record headers Index file dbase file Description of main file record contents Null shapes Point Multi point PolyLine Polygon PointM MultiPointM PolyLineM PolygonM PointZ MultiPointZ PolyLineZ PolyLineZ MultiPatch Satellite Tape Formats 3.30 Image Processing Software Introduction ERDAS Imagine Imagine essential Data types and integration Data visualization Geometric correction Simple classification Map composer General tools and utilities IMAGINE Advantage Ortho correction Metric accuracy assessment (MAA) tools Mosaicking Image processing Modeling language Knowledge classifier IMAGINE Professional Spectral analysis Expert classifier Multispectral classifier Radar interpreter System specifications of ERDAS IMAGINE ENVI Generally review ofenvi functionality Advantages of ENVI IDRISI 4.12

5 xiv IDRISI system overview Image processing menu Restoration submenu Enhancement submenu Transformation submenu Fourier analysis submenu Signature development submenu Hard classifiers submenu Soft classifiers / mixture analysis submenu Hyperspectral image analysis submenu Accuracy assessment submenu ER Mapper Algorithms View and enhance raster data Filters Contrast stretches (Transforms) Formulae and statistics View and edit vector data Integrate data Raster translators Automatic data fusion and mosaicing Hardcopy including stereo pair generation Map composition Classify raster images Visualize in 3-D Traverse Application based toolbars and batch scripts Raster to vector polygon conversion Geocoding Gridding Fourier transformation Customizable functions Virtual datasets Compression wizard Concluding Remarks 4.25 Initial Statistics Introduction Univariate Statistics Histogram Cumulative histogram Minimum and maximum value Mean and standard deviation Median Mode Skewness Kurtosis 5.6

6 variate xv 5.3 Multivariate Image Statistics Scatterplot Covariance matrix Correlation Illustrative Example Discussion on univariate statistics Multi - statistics Concluding remarks 5.14 Pre--Processing of Data Introduction Radiometric Corrections Missing scan lines De-striping methods Atmospheric Correction Methods Geometric Correction and Registration Orbital geometry model Aspect ratio Skew correction Earth rotation correction Transformation based on ground control points Resampling 6.12 Enhancement Techniques Introduction Contrast Stretch or Enhancement Linear Enhancement Min-Max stretch Percentile stretching Piece wise linear stretch Non Linear Enhancement Histogram equalization Gaussian equalization Logarithmic contrast enhancement Exponential contrast enhancement Comparison of Enhancement Method Illustrative Example 7.8 Image Transformations Basic Arithmetic Operators Image addition Image subtraction Image multiplication Image division Vegetation Indices Classification of Vegetation Indices 8.5

7 xvi 8.4 The Slope-based Vegetation Index Ratio vegetation index (RATIO) The normalized difference vegetation index (NDVI) The transformed vegetation index (TVI) The corrected transformed vegetation index (CTVI) Thiams transformed vegetation index (TTVI) Ratio vegetation index (RVI) The normalized ratio vegetation index (NRVI) Other indices The Distance-based Vegetation Index The perpendicular vegetation index (PVI) Difference vegetation index (DVI) The Ashburn vegetation index (AVI) The weighted difference vegetation index (WDVI) The soil-adjusted vegetation index (SAVI) The modified soil-adjusted vegetation indices (MSAVIi and MSAVI2) Atmospherically resistant vegetation index (ARVI) Soil and atmospherically resistant vegetation index (SARVI) Enhanced vegetation Index Special Indices Normalized difference water index Normalized difference snow index Normalized burn ratio The Orthogonal Transformations Principal component analysis Tasseled-cap components The concept of w-space indices Calculation of w-space coefficients Illustrative Example 8.40 Image Ratio and NDVI images Vegetation indices Transformed vegetation index Corrected transformed vegetation index Thiams transformed vegetation index Ratio vegetation index (RVI) Normalized ratio vegetation index Infrared index (II) Moisture stress Index Perpendicular vegetation Index Principal component analysis images Tassel cap transformation images 8.48 Classification Introduction Supervised Classification 9.2

8 xvii Classification scheme Training site selection and statistics extraction Guidelines for training data Idealized sequence for selecting training data Training data statistics Feature selection Selection of appropriate classification algorithm The parallelopiped classifier The minimum-distance to means classifier The maximum likelihood classifier Unsupervised Classification Distance based clustering methods Model-based clustering methods Density-based clustering method Condensation-based method for clustering Subspace clustering methods Feature selection for clustering Clustering pattern classification by distance functions Minimum distance pattern classification Maximin distance algorithm tf-means algorithm ISODATA algorithm Classification Accuracy Assessment Error matrix Illustration Example Selection of training dataset Feature selection Image classification Assessment of accuracy Spatial Filtering Introduction Process of Filtering Noise Removal Filtering Mean filter Weighted mean filter Median filter Mode filter Olympic filter Multi level median (MLM) filter P-median (PM) filter Adaptive mean P-median (AMPM) filter Edge Detection Classification ofedge Detection Techniques Non-directional Filters Laplacian filter High boost filter 10.12

9 xviii Simple Directional Filtering Gradient filtering Roberts operator Prewitt operator Sobel operator Kirsch operator Zero Crossing Filtering LoG filter DDoG filter References R.1 Index 1.1 About the Author A.l