REMOTE SENSING: MODELS AND METHODS FOR IMAGE PROCESSING
CONTENTS CHAPTER 1 THE NATURE OF REMOTE SENSING 1 1 INTRODUCTION 1 2 REMOTE SENSING 1 2 1 INFORMATION EXTRACTION FROM REMOTE-SENSING IMAGES 1 2 2 SPECTRAL FACTORS IN REMOTE SENSING 1 3 SPECTRAL SIGNATURES 1 4 REMOTE-SENSING SYSTEMS 1 4 1 SPATIAL AND RADIOMETRIC CHARACTERISTICS 1 4 2 SPECTRAL CHARACTERISTICS 1 4 3 TEMPORAL CHARACTERISTICS 1 4 4 MULTI-SENSOR FORMATION FLYING 1 5 IMAGE DISPLAY SYSTEMS 1 6 DATA SYSTEMS 1 7 SUMMARY 1 8 EXERCISES CHAPTER 2 OPTICAL RADIATION MODELS 2 1 INTRODUCTION 2 2 VISIBLE TO SHORTWAVE INFRARED REGION 2 2 1 SOLAR RADIATION 2 2 2 RADIATION COMPONENTS SURFACE-REFLECTED, UNSCATTERED COMPONENT SURFACE-REFLECTED, ATMOSPHERE-SCATTERED COMPONENT PATH-SCATTERED COMPONENT TOTAL AT-SENSOR, SOLAR RADIANCE 2 2 3 IMAGE EXAMPLES IN THE SOLAR REGION TERRAIN SHADING SHADOWING ATMOSPHERIC CORRECTION 2 3 MIDWAVE TO THERMAL INFRARED REGION 2 3 1 THERMAL RADIATION 2 3 2 RADIATION COMPONENTS SURFACE-EMITTED COMPONENT SURFACE-REFLECTED, ATMOSPHERE-EMITTED COMPONENT PATH-EMITTED COMPONENT TOTAL AT-SENSOR, EMITTED RADIANCE 2 3 3 TOTAL SOLAR AND THERMAL UPWELLING RADIANCE 2 3 4 IMAGE EXAMPLES IN THE THERMAL REGION 2 4 SUMMARY 2 5 EXERCISES CHAPTER 3 SENSOR MODELS 3 1 INTRODUCTION 3 2 OVERALL SENSOR MODEL 3 3 RESOLUTION 3 3 1 THE INSTRUMENT RESPONSE 3 3 2 SPATIAL RESOLUTION 3 3 3 SPECTRAL RESOLUTION 3 4 SPATIAL RESPONSE 3 4 1 OPTICAL PSFOPT 3 4 2 DETECTOR PSFDET 3 4 3 IMAGE MOTION PSFIM 3 4 4 ELECTRONICS PSFEL 3 4 5 NET PSFNET 3 4 6 COMPARISON OF SENSOR PSFS 3 4 7 IMAGING SYSTEM SIMULATION 3 4 8 MEASURING THE PSF ALI LSF MEASUREMENT QUICKBIRD LSF MEASUREMENT 3 5 SPECTRAL RESPONSE 3 6 SIGNAL AMPLIFICATION 3 7 SAMPLING AND QUANTIZATION 3 8 SIMPLIFIED SENSOR MODEL 3 9 GEOMETRIC DISTORTION 3 9 1 SENSOR LOCATION MODELS 3 9 2 SENSOR ATTITUDE MODELS 3 9 3 SCANNER MODELS 3 9 4 EARTH MODEL 3 9 5 LINE AND WHISKBROOM SCAN GEOMETRY 3 9 6 PUSHBROOM SCAN GEOMETRY 3 9 7 TOPOGRAPHIC DISTORTION 3 10 SUMMARY 3 11 EXERCISES CHAPTER 4 DATA MODELS 4 1 INTRODUCTION 4 2 A WORD ON NOTATION 4 3 UNIVARIATE IMAGE STATISTICS 4 3 1 HISTOGRAM NORMAL DISTRIBUTION 4 3 2 CUMULATIVE HISTOGRAM 4 3 3 STATISTICAL PARAMETERS 4 4 MULTIVARIATE IMAGE STATISTICS 4 4 1 REDUCTION TO UNIVARIATE STATISTICS 4 5 NOISE MODELS 4 5 1 STATISTICAL MEASURES OF IMAGE QUALITY CONTRAST MODULATION SIGNAL-TO-NOISE RATIO (SNR) NATIONAL IMAGERY INTERPRETABILITY SCALE (NIIRS) 4 5 2 NOISE EQUIVALENT SIGNAL 4 6 SPATIAL STATISTICS 4 6 1 VISUALIZATION OF SPATIAL COVARIANCE 4 6 2 COVARIANCE AND SEMIVARIOGRAM SEPARABILITY AND ANISOTROPY 4 6 3 POWER SPECTRAL DENSITY 4 6 4 CO-OCCURRENCE MATRIX 4 6 5 FRACTAL GEOMETRY 4 7 TOPOGRAPHIC AND SENSOR EFFECTS 4 7 1 TOPOGRAPHY AND SPECTRAL SCATTERGRAMS 4 7 2 SENSOR CHARACTERISTICS AND SPATIAL STATISTICS 4 7 3 SENSOR CHARACTERISTICS AND SPECTRAL SCATTERGRAMS 4 8 SUMMARY 4 9 EXERCISES CHAPTER 5 SPECTRAL TRANSFORMS 5 1 INTRODUCTION 5 2 FEATURE SPACE 5 3 MULTISPECTRAL RATIOS 5 3 1 VEGETATION INDEXES 5 3 2 IMAGE EXAMPLES 5 4 PRINCIPAL COMPONENTS 5 4 1 STANDARDIZED PRINCIPAL COMPONENTS (SPC) 5 4 2 MAXIMUM NOISE FRACTION (MNF) 5 5 TASSELED-CAP COMPONENTS 5 6 CONTRAST ENHANCEMENT 5 6 1 GLOBAL TRANSFORMS LINEAR STRETCH NONLINEAR STRETCH NORMALIZATION STRETCH REFERENCE STRETCH THRESHOLDING 5 6 2 LOCAL TRANSFORMS 5 6 3 COLOR IMAGES MIN-MAX STRETCH DECORRELATION STRETCH COLOR-SPACE TRANSFORMS SPATIAL DOMAIN BLENDING 5 7 SUMMARY 5 8 EXERCISES CHAPTER 6 SPATIAL TRANSFORMS 6 1 INTRODUCTION 6 2 AN IMAGE MODEL FOR SPATIAL FILTERING 6 3 CONVOLUTION FILTERS 6 3 1 LINEAR FILTERS CONVOLUTION LOW-PASS AND HIGH-PASS FILTERS (LPF, HPF) HIGH-BOOST FILTERS (HBF) BAND-PASS FILTERS (BPF) DIRECTIONAL FILTERS THE BORDER REGION CHARACTERISTICS OF FILTERED IMAGES APPLICATION OF THE BLENDING ALGORITHM TO SPATIAL FILTERING THE BOX-FILTER ALGORITHM CASCADED LINEAR FILTERS 6 3 2 STATISTICAL FILTERS MORPHOLOGICAL FILTERS 6 3 3 GRADIENT FILTERS 6 4 FOURIER TRANSFORMS 6 4 1 FOURIER ANALYSIS AND SYNTHESIS 6 4 2 DISCRETE FOURIER TRANSFORMS IN 2-D 6 4 3 THE FOURIER COMPONENTS 6 4 4 FILTERING WITH THE FOURIER TRANSFORM TRANSFER FUNCTIONS 6 4 5 SYSTEM MODELING USING THE FOURIER TRANSFORM 6 4 6 THE POWER SPECTRUM 6 5 SCALE-SPACE TRANSFORMS 6 5 1 IMAGE RESOLUTION PYRAMIDS 6 5 2 ZERO-CROSSING FILTERS LAPLACIAN-OF-GAUSSIAN (LOG) FILTERS DIFFERENCE-OF-GAUSSIANS (DOG) FILTERS 6 5 3 WAVELET TRANSFORMS 6 6 SUMMARY 6 7 EXERCISES CHAPTER 7 CORRECTION AND CALIBRATION 7 1 INTRODUCTION 7 2 DISTORTION CORRECTION 7 2 1 POLYNOMIAL DISTORTION MODELS GROUND CONTROL POINTS (GCPS) 7 2 2 COORDINATE TRANSFORMATION MAP PROJECTIONS 7 2 3 RESAMPLING 7 3 SENSOR MTF COMPENSATION 7 3 1 EXAMPLES OF MTF COMPENSATION 7 4 NOISE REDUCTION 7 4 1 GLOBAL NOISE SIGMA FILTER NAGAO-MATSUYAMA FILTER 7 4 2 LOCAL NOISE DETECTION BY SPECTRAL CORRELATION 7 4 3 PERIODIC NOISE 7 4 4 DETECTOR STRIPING GLOBAL, LINEAR DETECTOR MATCHING NONLINEAR DETECTOR MATCHING STATISTICAL MODIFICATION SPATIAL FILTER MASKING DEBANDING 7 5 RADIOMETRIC CALIBRATION 7 5 1 MULTISPECTRAL SENSORS AND IMAGERY SENSOR CALIBRATION SOLAR AND TOPOGRAPHIC CORRECTION IMAGE EXAMPLES 7 5 2 HYPERSPECTRAL SENSORS AND IMAGERY NORMALIZATION TECHNIQUES 7 6 SUMMARY 7 7 EXERCISES CHAPTER 8 REGISTRATION AND FUSION 8 1 INTRODUCTION 8 2 WHAT IS REGISTRATION? 8 3 AUTOMATED GCP LOCATION 8 3 1 AREA CORRELATION RELATION TO SPATIAL STATISTICS 8 3 2 OTHER SPATIAL FEATURES FOR REGISTRATION 8 4 ORTHORECTIFICATION 8 4 1 LOW-RESOLUTION DEM 8 4 2 HIGH-RESOLUTION DEM HIERARCHICAL WARP STEREO 8 5 MULTI-IMAGE FUSION 8 5 1 FEATURE SPACE FUSION 8 5 2 SPATIAL DOMAIN FUSION HIGH FREQUENCY MODULATION FILTER DESIGN FOR HFM SHARPENING WITH A SENSOR MODEL 8 5 3 SCALE-SPACE FUSION 8 5 4 IMAGE FUSION EXAMPLES 8 6 SUMMARY 8 7 EXERCISES CHAPTER 9 THEMATIC CLASSI?CATION 9 1 INTRODUCTION 9 2 THE CLASSIFICATION PROCESS 9 2 1 THE IMPORTANCE OF IMAGE SCALE AND RESOLUTION 9 2 2 THE NOTION OF SIMILARITY 9 2 3 HARD VERSUS SOFT CLASSI?CATION 9 3 FEATURE EXTRACTION 9 4 TRAINING THE CLASSIFIER 9 4 1 SUPERVISED TRAINING SEPARABILITY ANALYSIS 9 4 2 UNSUPERVISED TRAINING K-MEANS CLUSTERING ALGORITHM CLUSTERING EXAMPLES 9 4 3 HYBRID SUPERVISED/UNSUPERVISED TRAINING 9 5 NONPARAMETRIC CLASSIFICATION 9 5 1 LEVEL-SLICE CLASSI?ER 9 5 2 HISTOGRAM ESTIMATION CLASSI?ER 9 5 3 NEAREST-NEIGHBORS CLASSI?ER 9 5 4 ARTI?CIAL NEURAL NETWORK (ANN) CLASSI?ER BACK-PROPAGATION ALGORITHM 9 5 5 NONPARAMETRIC CLASSI?CATION EXAMPLES 9 6 PARAMETRIC CLASSIFICATION 9 6 1 ESTIMATION OF MODEL PARAMETERS 9 6 2 DISCRIMINANT FUNCTIONS 9 6 3 THE NORMAL DISTRIBUTION MODEL 9 6 4 THE NEAREST-MEAN CLASSI?ER 9 6 5 PARAMETRIC CLASSI?CATION EXAMPLES 9 7 SPATIAL-SPECTRAL SEGMENTATION 9 7 1 REGION GROWING 9 8 SUBPIXEL CLASSIFICATION 9 8 1 THE LINEAR MIXING MODEL UNMIXING EXAMPLES RELATION OF FRACTIONS TO NEURAL NETWORK OUTPUT ENDMEMBER SPECIFICATION 9 8 2 FUZZY SET CLASSI?CATION FUZZY C-MEANS (FCM) CLUSTERING FUZZY SUPERVISED CLASSIFICATION 9 9 HYPERSPECTRAL IMAGE ANALYSIS 9 9 1 VISUALIZATION OF THE IMAGE CUBE 9 9 2 TRAINING FOR CLASSI?CATION 9 9 3 FEATURE EXTRACTION FROM HYPERSPECTRAL DATA IMAGE RESIDUALS ABSORPTION-BAND PARAMETERS SPECTRAL DERIVATIVE RATIOS SPECTRAL FINGERPRINTS 9 9 4 CLASSI?CATION ALGORITHMS FOR HYPERSPECTRAL DATA BINARY ENCODING SPECTRAL-ANGLE MAPPING ORTHOGONAL SUBSPACE PROJECTION (OSP) 9 10 SUMMARY 9 11 EXERCISES