MARC Record from marc_columbia

LEADER: 28657cam a2200481 i 4500
001 13603798
005 20181218122943.0
008 150220t20162016ilua b 001 0 eng
010 $a 2015007022
015 $aGBB550555$2bnb
016 7 $a017163947$2Uk
020 $a9780134058160
020 $a013405816X
035 $a(OCoLC)ocn917874007
035 $a(OCoLC)917874007
035 $a(NNC)13603798
040 $aDLC$beng$erda$cSTF$dOCLCO$dOCLCF$dZ5A$dAU@$dUKMGB
042 $apcc
050 00 $aG70.4$b.J46 2016
082 00 $a006.4/2$223
100 1 $aJensen, John R.,$d1949-$eauthor.
245 10 $aIntroductory digital image processing :$ba remote sensing perspective /$cJohn R. Jensen (University of South Carolina).
246 30 $aDigital image processing
250 $a4th edition.
264 1 $aGlenview, IL :$bPearson Education, Inc.,$c[2016]
264 4 $c©2016
300 $axxxi, 623 pages :$bcolor illustrations ;$c28 cm.
336 $atext$2rdacontent
337 $aunmediated$2rdamedia
338 $avolume$2rdacarrier
490 1 $aPearson series in geographic information science
504 $aIncludes bibliographical references and index.
505 00 $aMachine-generated contents note:$tOverview --$tIn Situ Data Collection --$tRemote-Sensing Data Collection --$tObservations About Remote-Sensing --$tRemote-Sensing: Art and/or Science? --$tInformation About an Object or Area --$tThe Instrument (Sensor) --$tDistance: How Far Is Remote? --$tRemote-Sensing Advantages and Limitations --$tAdvantages --$tLimitations --$tThe Remote-Sensing Process --$tStatement of the Problem --$tIdentification of In situ and Remote-Sensing Data Requirements --$tCollateral Data Requirements --$tRemote-Sensing Data Requirements --$tRemote-Sensing Data Collection --$tSpectral Information and Resolution --$tSpatial Information and Resolution --$tTemporal Information and Resolution --$tRadiometric Information and Resolution --$tPolarization Information --$tAngular Information --$tSub-orbital (Airborne) Remote-Sensing Systems --$tSatellite Remote-Sensing Systems --$tRemote-Sensing Data Analysis --$tAnalogue (Visual) Image Processing --$tDigital Image Processing --$tInformation Presentation --$tEarth Observation Economics --$tRemote-Sensing/Digital Image-Processing Careers in the Public and Private Sectors --$tEarth Resource Analysis Perspective --$tBook Organization --$tReferences --$tOverview --$tAnalogue (Hard-Copy) Image Digitization --$tDigital Image Terminology --$tMicrodensitometer Digitization --$tVideo Digitization --$tLinear and Area Array Charge-Coupled-Device Digitization --$tDigitized National Aerial Photography Program (NAPP) Data --$tDigitization Considerations --$tDigital Remote Sensor Data Collection --$tMultispectral Imaging Using Discrete Detectors and Scanning Mirrors --$tMultispectral Imaging Using Linear Arrays --$tImaging Spectrometry Using Linear and Area Arrays --$tAirborne Digital Cameras --$tSatellite Analogue and Digital Photographic Systems --$tMultispectral Imaging Using Discrete Detectors and Scanning Mirrors --$tEarth Resource Technology Satellites and Landsat 1-7 Sensor Systems --$tLandsat Multispectral Scanner --$tLandsat Thematic Mapper (TM) --$tLandsat 7 Enhanced Thematic Mapper Plus --$tNOAA Multispectral Scanner Sensors --$tGeostationary Operational Environmental Satellite (GOES) --$tAdvanced Very-High Resolution Radiometer --$tNOAA Suomi NPOESS Preparatory Project (NPP) --$tSeaStar Satellite and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) --$tSeaWiFS --$tMultispectral Imaging Using Linear Arrays --$tNASA Earth Observing-1 (EO-1) Advanced Land Imager (ALI) --$tAdvanced Land Imager (ALI) --$tNASA Landsat 8 (LDCM -- Landsat Data Continuity Mission) --$tOrbital Land Imager --$tSPOT Sensor Systems --$tSPOT 1, 2, and 3 --$tSPOT 4 and 5 --$tSPOT 6 and 7 --$tPleiades --$tPleiades 1A and 1B --$tIndian Remote-Sensing Systems --$tIRS-1A, -1B, -1C, and -1D --$tCartoSat --$tResourceSat --$tKorean Aerospace Research Institute (KARI) KOMPSATs --$tAstrium, Inc. Sentinel-2 --$tAdvanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) --$tMultiangle Imaging Spectroradiometer (MISR) --$tGeoEye, Inc. (formerly Space Imaging, Inc.), IKONOS-2, GeoEye-1, GeoEye-2 --$tIKONOS-1 and -2 --$tGeoEye-1 and -2 --$tEarthWatch/DigitalGlobe, Inc., QuickBird, WorldView-1, WorldView-2, WorldView-3 --$tQuickBird --$tWorld-View-1, -2, and -3 --$tImageSat International, Inc., EROS A and EROS B --$tEROS A and EROS B --$tRapidEye, Inc. --$tRapidEye --$tDMC International Imaging, Ltd., SLIM-6 and NigeriaSat-2 --$tSLIM-6 --$tDMC-NigeriaSat-2 --$tImaging Spectrometry Using Linear and Area Arrays --$tNASA EO-1 Hyperion Hyperspectral Imager --$tHyperion --$tNASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) --$tAVIRIS --$tModerate Resolution-Imaging Spectrometer (MODIS) --$tNASA Hyperspectral Infrared Imager (HyspiRl) --$tItres, Inc. Compact Airborne Spectrographic Imager-1500 --$tCASI-1500 --$tSASI-600 --$tMASI-600 --$tTASI-600 --$tHyVista, Inc., HyMap --$tAirborne Digital Cameras --$tSmall-Format Digital Cameras --$tMedium-Format Digital Cameras --$tLeica Geosystems, Ag., RCD30 --$tLarge-Format Digital Cameras --$tLeica Geosystems, Ag., ADS80, Z/I Imaging DMC Aerial Photography --$tMicrosoft, Inc., UltraCam Eagle --$tDigital Oblique Aerial Photography --$tPictometry International, Inc., Oblique and Vertical Aerial Photography --$tSatellite Digital Frame Camera Systems --$tU.S. Space Shuttle Photography --$tSpace Shuttle Analog Cameras --$tSpace Shuttle and Space Station Digital Photography --$tDigital Image Data Formats --$tBand Interleaved by Pixel Format --$tBand Interleaved by Line Format --$tBand Sequential Format --$tSummary --$tReferences --$tOverview --$tDigital Image-Processing Hardware Considerations --$tCentral Processing Unit Considerations --$tHistory of Central Processing Units and Efficiency Measurement --$tType of Computer --$tPersonal Computers --$tComputer Workstations --$tMainframe Computers --$tRead-Only Memory and Random Access Memory --$tSerial and Parallel Image Processing --$tMode of Operation and User Interface --$tMode of Operation --$tInteractive Graphical User Interface --$tBatch Processing --$tComputer Operating System and Compiler(s) --$tInput Devices --$tOutput Devices --$tData Storage and Archiving Considerations --$tRapid-Access Mass Storage --$tArchiving Considerations-Longevity --$tComputer Display Spatial and Colour Resolution --$tComputer Screen Display Resolution --$tComputer Screen Colour Resolution --$tDigital Image Processing Software Considerations --$tImage Processing Functions --$tDigital Image Processing Software --$tMultispectral Digital Image Processing Software --$tGeographic Object-based Image Analysis (GEOBIA) --$tHyperspectral Digital Image Processing Software --$tLiDAR Digital Image Processing Software --$tRADAR Digital Image Processing Software --$tPhotogrammetric Mapping Software --$tChange Detection --$tIntegration of Digital Image Processing and GIS Functions --$tCost --$tOpen-Source Digital Image Processing Software --$tOpen-Source Statistical Analysis Software that can be used for Digital Image Processing --$tDigital Image Processing and the National Spatial Data Infrastructure --$tReferences --$tOverview --$tImage Processing Mathematical Notation --$tSampling Theory --$tTypes of Sampling --$tThe Histogram and its Significance to Digital Image Processing --$tMetadata --$tViewing Individual Pixel Values at Specific Locations or within a Geographic Area --$tCursor Evaluation of Individual Pixel Brightness Values --$tTwo- and Three-dimensional Evaluation of Pixel Brightness Values within a Geographic Area --$tUnivariate Descriptive Image Statistics --$tMeasure of Central Tendency in Remote Sensor Data --$tMeasures of Dispersion --$tMeasures of Distribution (Histogram) Asymmetry and Peak Sharpness --$tMultivariate Image Statistics --$tCovariance in Multiple Bands of Remote Sensor Data --$tCorrelation between Multiple Bands of Remotely-Sensed Data --$tFeature Space Plots --$tGeostatistical Analysis, Autocorrelation and Kriging Interpolation --$tCalculating Average Semi-variance --$tEmpirical Semi-variogram --$tReferences --$tOverview --$tImage Display Considerations --$tBlack-and-White Hard-Copy Image Display --$tLine Printer/Plotter Brightness Maps --$tLaser or Ink-Jet Printer Brightness Maps --$tTemporary Video Image Display --$tBlack-and-White and Colour Brightness Maps --$tImage Data Format and Compression Scheme --$tBitmapped Graphics --$tRGB Colour Coordinate System --$tColour Look-Up Tables: 8-bit --$tColour Look-Up Tables: 24-bit --$tColour Composites --$tOptimum Index Factor --$tSheffield Index --$tIndependent Component Analysis-Based Fusion for Colour Display of Hyperspectral Images --$tMerging (Fusing) Remotely-Sensed Data --$tSimple Band Substitution --$tColour Space Transformation and Component Substitution --$tRGB to IHS Transformation and Back Again --$tChromaticity Colour Coordinate System and the Brovey Transformation --$tPrincipal Component Analysis (PCA), Independent Component Analysis (ICA), or Gram-Schmidt Substitution --$tPixel-by-Pixel Addition of High-Frequency Information --$tFusion based on Regression Kriging --$tSmoothing Filter-Based Intensity Modulation Image Fusion --$tLength (Distance) Measurement --$tLinear Distance Measurement Based on the Pythagorean Theorem --$tManhattan Distance Measurement --$tPerimeter, Area, and Shape Measurement --$tPerimeter Measurement --$tArea Measurement --$tShape Measurement --$tReferences --$tOverview --$tElectromagnetic Energy Interactions --$tConduction, Convection, and Radiation --$tElectromagnetic Radiation Models --$tWave Model of Electromagnetic Energy --$tThe Particle Model: Radiation from Atomic Structures --$tAtmospheric Energy-Matter Interactions --$tRefraction --$tScattering --$tAbsorption --$tReflectance --$tTerrain Energy-Matter Interactions --$tHemispherical Reflectance, Absorptance, and Transmittance --$tRadiant Flux Density --$tIrradiance and Exitance --$tRadiance --$tEnergy-Matter Interactions in the Atmosphere Once Again --$tEnergy-Matter Interactions at the Sensor System --$tCorrecting Remote-Sensing Detector Error --$tRandom Bad Pixels (Shot Noise) --$tLine or Column Drop-Outs --$tPartial Line or Column Drop-Outs --$tLine-Start Problems --$tN-Line Striping --$tRemote-Sensing Atmospheric Correction --$tUnnecessary Atmospheric Correction --$tNecessary Atmospheric Correction --$tTypes of Atmospheric Correction --$tAbsolute Radiometric Correction of Atmospheric Attenuation --$tTarget and Path Radiance --$tAtmospheric Transmittance --$tDiffuse Sky Irradiance --$tAtmospheric Correction Based on Radiative Transfer Modelling --$tAbsolute Atmospheric Correction Using Empirical Line Calibration --$tRelative Radiometric Correction of Atmospheric Attenuation
505 00 $aNote continued:$tSingle-Image Normalization Using Histogram Adjustment --$tMultiple-Date Image Normalization Using Regression --$tCorrecting for Slope and Aspect Effects --$tThe Cosine Correction --$tThe Minnaert Correction --$tA Statistical-Empirical Correction --$tThe C Correction --$tLocal Correlation Filter --$tReferences --$tInternal and External Geometric Error --$tInternal Geometric Error --$tImage Offset (Skew) Caused by Earth Rotation Effects --$tScanning System-Induced Variation in Ground Resolution Cell-Size --$tScanning System One-Dimensional Relief Displacement --$tScanning System Tangential Scale Distortion --$tExternal Geometric Error --$tAltitude Changes --$tAttitude Changes --$tGround Control Points --$tTypes of Geometric Correction --$tImage-to-Map Rectification --$tImage-to-Image Registration --$tHybrid Approach to Image Rectification/Registration --$tImage-to-Map Geometric Rectification Logic --$tSpatial Interpolation Using Coordinate Transformations --$tIntensity Interpolation --$tAn Example of Image-to-Map Rectification --$tSelecting an Appropriate Map Projection --$tDevelopable Surfaces used to Create Map Projections --$tMap Projection Characteristics --$tCylindrical Map Projections --$tAzimuthal (Planar) Map Projections --$tConical Map Projections --$tOther Projections and Coordinate Systems Useful for Image Rectification --$tGround Control Point Collection --$tDetermine Optimum Geometric Rectification Coefficients by Evaluating GCP Total RMSerror --$tMultiple Regression Coefficients Computation --$tFill Output Matrix Using Spatial and Intensity Interpolation Resampling --$tMosaicking --$tMosaicking Rectified Images --$tConclusion --$tReferences --$tOverview --$tImage Reduction and Magnification --$tImage Reduction --$tImage Magnification --$tTransects (Spatial Profiles) --$tSpectral Profiles --$tContrast Enhancement --$tLinear Contrast Enhancement --$tMinimum-Maximum Contrast Stretch --$tPercentage Linear and Standard Deviation Contrast Stretching --$tPiecewise Linear Contrast Stretch --$tNon-linear Contrast Enhancement --$tBand Ratioing --$tNeighbourhood Raster Operations --$tQualitative Raster Neighbourhood Modelling --$tQuantitative Raster Neighbourhood Modelling --$tSpatial Filtering --$tSpatial Convolution Filtering --$tLow-frequency Filtering in the Spatial Domain --$tHigh-frequency Filtering in the Spatial Domain --$tEdge Enhancement in the Spatial Domain --$tThe Fourier Transform --$tSpatial Filtering in Frequency Domain --$tPrincipal Components Analysis (PCA) --$tVegetation Indices (VI) --$tDominant Factors Controlling Leaf Reflectance --$tVisible Light Interaction with Pigments in the Palisade Mesophyll Cells --$tNear-Infrared Energy Interaction within the Spongy Mesophyll Cells --$tMiddle-Infrared Energy Interaction with Water in the Spongy Mesophyll --$tRemote Sensing-Derived Vegetation Indices --$tSimple Ratio-SR --$tNormalized Difference Vegetation Index-NDVI --$tKauth-Thomas Tasselled Cap Transformation --$tNormalized Difference Moisture or Water Index-NDMI or NDWI --$tPerpendicular Vegetation Index-PVI --$tLeaf Water Content Index-LWCI --$tSoil-Adjusted Vegetation Index-SAVI --$tAtmospherically-Resistant Vegetation Index-ARVI --$tSoil and Atmospherically-Resistant Vegetation Index-SARVI --$tAerosol-Free Vegetation Index-AFRI --$tEnhanced Vegetation Index-EVI --$tTriangular Vegetation Index-TVI --$tReduced Simple Ratio-RSR --$tChlorophyll Absorption in Reflectance Index-CARI --$tModified Chlorophyll Absorption in Reflectance Index-MTCARI --$tOptimized Soil-Adjusted Vegetation Index-OSAVI --$tRatio TCARI/OSAVI --$tVisible Atmospherically-Resistant Index-VARI --$tNormalized Difference Built-Up Index-NDBI --$tVegetation-Adjusted Night-time Light (NTL) Urban Index-VANUI --$tRed-Edge Position Determination-REP --$tPhotochemical Reflectance Index-PRI --$tNDVI and Cellulose Absorption Index-CAI --$tMERIS Terrestrial Chlorophyll Index-MTCI --$tNormalized Burn Ratio-NBR --$tVegetation Suppression --$tTexture Transformations --$tFirst-Order Statistics in the Spatial Domain --$tEdge-Preserving Spectral-Smoothing (EPSS) Variance Texture --$tConditional Variance Detection --$tMin-Max Texture Operator --$tMoran's I Spatial Autocorrelation as a Texture Measure --$tSecond-Order Statistics in the Spatial Domain --$tTexture Units as Elements of a Texture Spectrum --$tTexture Statistics Based on the Semi-variogram --$tLandscape Ecology Metrics --$tLandscape Indicators and Patch Metrics --$tReferences --$tOverview --$tIntroduction --$tSupervised Classification --$tLand-Use and Land-Cover Classification Schemes --$tAmerican Planning Association Land-Based Classification Standard (LBCS) --$tUSGS Anderson Land-Use/Land-Cover Classification System for Use with Remote Sensor Data --$tNational Land Cover Database (NLCD) Classification System --$tNOAA Coastal Change Analysis Program (C-CAP) Classification Scheme --$tU.S. Department of the Interior Fish & Wildlife Service Classification of Wetlands and Deepwater Habitats of the United States --$tU.S. National Vegetation Classification Standard (NVCS) --$tInternational Geosphere-Biosphere Program IGBP Land-Cover Classification System Modified for the Creation of MODIS Land-Cover Type Products --$tObservations about Classification Schemes --$tTraining Site Selection and Statistics Extraction --$tSelecting the Optimum Bands for Image Classification: Feature Selection --$tGraphic Methods of Feature Selection --$tStatistical Methods of Feature Selection --$tSelect the Appropriate Classification Algorithm --$tParallelepiped Classification Algorithm --$tMinimum Distance to Means Classification Algorithm --$tNearest-Neighbour Classifiers --$tMaximum Likelihood Classification Algorithm --$tUnsupervised Classification --$tUnsupervised Classification Using the Chain Method --$tPass 1: Cluster Building --$tPass 2: Assignment of Pixels to One of the Cmax Clusters Using Minimum-Distance Classification --$tUnsupervised Classification Using the ISODATA Method --$tISODATA Initial Arbitrary Cluster Allocation --$tISODATA First Iteration 408; Second to Mth Iteration --$tUnsupervised Cluster Busting --$tFuzzy Classification --$tObject-Based Image Analysis (OBIA) Classification --$tGeographic Object-Based Image Analysis and Classification --$tOBIA Classification Considerations --$tIncorporating Ancillary Data in the Classification Process --$tProblems Associated with Ancillary Data --$tApproaches to Incorporating Ancillary Data to Improve Remote Sensing Classification Maps --$tGeographical Stratification --$tClassifier Operations --$tPost-Classification Sorting --$tReferences --$tOverview --$tExpert Systems --$tExpert System User Interface --$tCreating the Knowledge Base --$tAlgorithmic Approaches to Problem Solving --$tHeuristic Knowledge-Based Expert System Approaches to Problem Solving --$tThe Knowledge Representation Process --$tInference Engine --$tOn-Line Databases --$tExpert Systems Applied to Remote Sensor Data --$tDecision-Tree Classification Based on Human-Derived Rules --$tHypotheses to Be Tested --$tRules (Variables) --$tConditions --$tInference Engine --$tClassification Based on Machine Learning Decision Trees and Regression Trees --$tMachine Learning --$tDecision-Tree Training --$tDecision-Tree Generation --$tFrom Decision Trees to Production Rules --$tCase Study --$tAdvantages of Decision-Tree Classifiers --$tRandom Forest Classifier --$tSupport Vector Machines --$tNeural Networks --$tComponents and Characteristics of a Typical Artificial Neural Network Used to Extract Information from Remotely-Sensed Data --$tTraining an Artificial Neural Network --$tTesting (Classification) --$tMathematics of the Artificial Neural Network --$tFeed Forward Multi-Layer Perceptron (MLP) Neural Network with Back Propagation (BP) --$tKohonen's Self-Organizing Map (SOM) Neural Network --$tFuzzy ARTMAP Neural Network --$tAdvantages of Artificial Neural Networks --$tLimitations of Artificial Neural Networks --$tReferences --$tOverview --$tPanchromatic, Multispectral and Hyperspectral Data Collection --$tPanchromatic --$tMultispectral --$tHyperspectral --$tSatellite Hyperspectral Sensors --$tAirborne Optical Hyperspectral Sensors --$tAirborne Thermal-Infrared Hyperspectral Sensors --$tSteps to Extract Information from Hyperspectral Data --$tSelect Study Area from Flight Lines --$tInitial Image Quality Assessment --$tVisual Examination of Hyperspectral Colour Composite Images --$tVisual Individual Band Examination --$tAnimation --$tStatistical Individual Band Examination --$tRadiometric Calibration --$tIn Situ Data Collection --$tAbsolute Atmospheric Correction --$tRadiative Transfer-Based Absolute Atmospheric Correction --$tAbsolute Atmospheric Correction using Empirical Line Calibration --$tGeometric Correction of Hyperspectral Remote Sensor Data --$tReducing the Dimensionality of Hyperspectral Data --$tMinimum Noise Fraction (MNF) Transformation --$tEndmember Determination: Locating the Spectrally-Purest Pixels --$tPixel Purity Index Mapping --$tn-Dimensional Endmember Visualization --$tMapping and Matching using Hyperspectral Data --$tSpectral Angle Mapper --$tSubpixel Classification, Linear Spectral Unmixing or Spectral Mixture Analysis --$tContinuum Removal --$tSpectroscopic Library Matching Techniques --$tMachine Learning Analysis of Hyperspectral Data --$tDecision-Tree Analysis of Hyperspectral Data --$tSupport Vector Machine (SVM) Analysis of Hyperspectral Data --$tSelected Indices Useful for Hyperspectral Data Analysis --$tReduced Simple Ratio --$tNormalized Difference Vegetation Index-NDVI --$tHyperspectral Enhanced Vegetation Index-EVI --$tYellowness Index-YI --$tPhysiological Reflectance Index-PRI
505 00 $aNote continued:$tNormalized Difference Water Index-NDWI --$tLinear Red-Edge Position-REP --$tRed-Edge Vegetation Stress Index (RVSI) --$tCrop Chlorophyll Content Prediction --$tModified Chlorophyll Absorption Ratio Index (MCARI1) --$tChlorophyll Index --$tMedium-Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) --$tDerivative Spectroscopy --$tNarrow-Band Derivative-Based Vegetation Indices --$tRed-Edge Position Based on Derivative Ratio --$tReferences --$tOverview --$tSteps Required to Perform Change Detection --$tSpecify the Thematic Attribute(s) or Indicator(s) of Interest --$tSpecify the Change Detection Geographic Region of Interest (ROI) --$tSpecify the Change Detection Time Period --$tSelect an Appropriate Land-Use/Land-Cover Classification System --$tSelect Hard (Crisp) and/or Soft (Fuzzy) Change Detection Logic --$tSelect Per-pixel or Object-Based Change Detection (OBCD) --$tRemote Sensing System Change Detection Considerations --$tTemporal Resolution --$tLook Angle --$tSpatial Resolution --$tSpectral Resolution --$tRadiometric Resolution --$tEnvironmental/Developmental Considerations of Importance When Performing Change Detection --$tAtmospheric Conditions --$tSoil Moisture Conditions --$tPhenological Cycle Characteristics --$tObscuration Considerations --$tEffects of Tidal Stage on Change Detection --$tSelect the Most Appropriate Change Detection Algorithm --$tBinary Change Detection Algorithms Provide "Change/No-Change" Information --$tAnalogue "On-Screen" Visualization Change Detection --$tEsri, Inc., ChangeMatters® --$tBinary Change Detection Using Image Algebra --$tImage Differencing Change Detection --$tImage Algebra Band Ratioing Change Detection --$tImage Algebra Change Detection Using Statistical or Symmetric Thresholds --$tImage Algebra Change Detection Using Asymmetric Thresholds --$tImage Algebra Change Detection Using Moving Threshold Windows (MTW) --$tMultiple-Date Composite Image Change Detection --$tSupervised and Unsupervised Classification of Multiple-Date Composite Image to Detect Change --$tPrincipal Components Analysis (PCA) Composite Image Change Detection --$tMDA Information Systems, LLC., National Urban Change Indicator (NUCI)® --$tContinuous Change Detection and Classification (CCDC) using Landsat Data --$tThematic "From-To" Change Detection Algorithms --$tPhotogrammetric Change Detection --$tLiDARgrammetric Change Detection --$tPost-Classification Comparison Change Detection --$tPer-Pixel Post-Classification Comparison --$tOBIA Post-Classification Comparison --$tNeighbourhood Correlation Image (NCI) Change Detection --$tSpectral Change Vector Analysis --$tChange Detection Using an Ancillary Data Source as Date 1 --$tChange Detection Using a Binary Change Mask Applied to Date 2 --$tChi-Square Transformation Change Detection --$tCross-Correlation Change Detection --$tVisual On-Screen Change Detection and Digitization --$tHurricane Hugo Example --$tHurricane Katrina Example --$tAral Sea Example --$tNational Land Use/Cover Database of China Example --$tAtmospheric Correction for Change Detection --$tWhen Atmospheric Correction Is Necessary --$tWhen Atmospheric Correction Is Unnecessary --$tSummary --$tReferences --$tOverview --$tSteps to Perform Accuracy Assessment --$tSources of Error in Remote Sensing-Derived Thematic Maps --$tThe Error Matrix --$tTraining versus Ground Reference Test Information --$tSample Size --$tSample Size Based on Binomial Probability Theory --$tSample Size Based on Multinomial Distribution --$tSampling Design (Scheme) --$tSimple Random Sampling --$tSystematic Sampling --$tStratified Random Sampling --$tStratified Systematic Unaligned Sampling --$tCluster Sampling --$tObtaining Ground Reference Information at Locations Using a Response Design --$tEvaluation of Error Matrices --$tDescriptive Evaluation of Error Matrices --$tDiscrete Multivariate Techniques Applied to the Error Matrix --$tKappa Analysis --$tFuzzification of the Error Matrix --$tChange Detection Map Accuracy Assessment --$tAssessing the Accuracy of the Individual Thematic Maps used in a Change Detection Study --$tAssessing the Accuracy of a "From-To" Change Detection Map --$tResponse Design --$tSampling Design --$tAnalysis --$tAssessing the Accuracy of a Binary Change Detection Map --$tAssessing the Accuracy of an Object-Based Image Analysis (OBIA) Classification Map --$tGeostatistical Analysis in Support of Accuracy Assessment --$tImage Metadata and Lineage Information for Remote Sensing-Derived Products --$tIndividual Image Metadata --$tLineage of Remote Sensing-Derived Products --$tReferences --$tTable of Contents --$tFederal Image and Geospatial Data Search Engines and Repositories --$tCommercial Image and Geospatial Data Search Engines and/or Repositories --$tDigital Elevation Data --$tHydrography Data --$tLand Use/Land Cover and Biodiversity/Habitat Data --$tPopulation Demographic Data --$tRemote Sensor Data -- Public --$tRemote Sensor Data -- Commercial and International --$tFederal Geospatial Data Search Engines and Repositories --$tUSGS EarthExplorer --$tUSGS The National Map --$tUSGS Global Visualization Viewer --$tData.gov --$tCommercial Geospatial Data Search Engines and/or Repositories --$tGoogle, Inc., Google Earth Search Engine --$tMicrosoft, Inc., Bing Search Engine --$tEsri, Inc., ArcGIS Online Map and Geoservices --$tEsri Map Services --$tEsri Image Services --$tDigital Elevation Data --$tGTOP030 --$tNED-National Elevation Dataset --$tTopographic-Bathymetric Information --$tTopographic Change Information --$tSRTM-Shuttle RADAR Topography Mission --$tASTER Global Digital Elevation Model (GDEM V2) --$tNEXTMap World 30 DSM (Intermap, Inc.) --$tHydrography Data --$tNHD-National Hydrography Dataset --$tEDNA-Elevation Derivatives for National Applications --$tLand Use/Land Cover and Biodiversity/Habitat Data --$tNLCD-National Land Cover Database 1992, 2001, 2006, 2011 --$tC-CAP-Coastal Change Analysis Program --$tGAP Analysis Program --$tNWI-National Wetlands Inventory --$tRoad Network and Population Demographic Data --$tMAF/TIGER Line --$t2010 Census Population Demographics --$tLandScan Population Distribution Modelling --$tRemote Sensor Data-Public --$tASTER-Advanced Spaceborne Thermal Emission and Reflection Radiometer --$tAVHRR-Advanced Very-High Resolution Radiometer --$tAVIRIS-Airborne Visible Imaging Spectrometer --$tDeclassified Satellite Imagery --$tDOQ-Digital Orthophoto Quadrangles --$tLandsat-MSS, TM, ETM+, Landsat 8 --$tLiDAR-Light Detection and Ranging --$tMODIS-Moderate Resolution Imaging Spectrometer --$tNAIP-National Agriculture Imagery Program --$tSuomi-NPOESS Preparatory Project --$tRemote Sensor Data-Commercial and International --$tCASI-1500 --$tSASI-600 --$tMASI-600 --$tTASI-600 --$tEROS A and B --$tGeoEye-1 and -2 --$tHyMap --$tIKONOS-2 --$tIndian IRS-1A, -1B, -1C and -1D --$tIndian CartoSat-1, -2, -2A, -2B, and -3 --$tResourceSat-1 and -2 --$tKorean KOMPSAT1-5 --$tPICTOMETRY --$tPleiades-1 and -2 --$tQuickBird --$tRapidEye --$tSentinel-2 --$tSPOT 1-7 --$tWorldView-1, -2, and -3.
520 $aFor junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects.
650 0 $aRemote sensing.
650 0 $aImage processing$xDigital techniques.
650 7 $aImage processing$xDigital techniques.$2fast$0(OCoLC)fst00967508
650 7 $aRemote sensing.$2fast$0(OCoLC)fst01094469
830 0 $aPearson Prentice Hall series in geographic information science.
856 41 $uhttp://www.gbv.de/dms/tib-ub-hannover/81891579x.pdf$3Table of Contents
852 00 $bsci$hG70.4$i.J46 2016