Image Preprocessing for Remote Sensing

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image-preprocessing atmospheric-correction geometric-correction radiometric-calibration

Core Idea

Raw satellite imagery contains systematic distortions that must be corrected before meaningful analysis. Preprocessing transforms raw sensor data into scientifically usable products through three main steps: radiometric calibration (converting raw digital numbers to physical units of radiance or reflectance), atmospheric correction (removing the atmosphere's contribution to isolate the surface signal), and geometric correction (aligning image pixels to accurate ground coordinates). Without preprocessing, pixel values do not represent true surface properties, multi-date comparisons are invalid, and data from different sensors cannot be combined.

Explainer

Raw satellite data straight from the sensor is not ready for analysis. It contains a mixture of surface information, atmospheric effects, and geometric distortions that must be systematically separated and corrected. This preprocessing chain is the unglamorous but essential foundation of all quantitative remote sensing.

Radiometric calibration converts raw digital numbers (DN) to physical units. Each sensor has calibration coefficients that convert DN to at-sensor radiance (watts per square meter per steradian per micrometer). From radiance, dividing by the solar irradiance at the top of the atmosphere (adjusted for Earth-Sun distance and solar zenith angle) yields top-of-atmosphere (TOA) reflectance -- a standardized quantity that removes sensor-specific and illumination effects but still includes the atmosphere.

Atmospheric correction is the most scientifically important step. The atmosphere scatters incoming sunlight into the sensor's field of view (path radiance), absorbs portions of both downwelling and upwelling radiation, and alters the spectral distribution of light reaching the surface. Physics-based models (6S, MODTRAN, libRadtran) simulate these processes using atmospheric parameters (aerosol optical depth, water vapor column, ozone) to estimate and remove the atmospheric contribution. The result is surface reflectance -- what the surface would look like if there were no atmosphere.

Geometric correction ensures that pixels map to correct geographic locations. Satellite ephemeris data provides an initial geometric model, but systematic and non-systematic errors require correction using ground control points and, for accurate results, orthorectification using a DEM to remove terrain-induced displacement. The result is an image where each pixel has a reliable geographic coordinate, enabling overlay with other geospatial data and precise multi-temporal registration essential for change detection.

Practice Questions 3 questions

Prerequisite Chain

Counting to 10Counting to 20Understanding ZeroThe Number ZeroCounting to FiveOne-to-One CorrespondenceCombining Small Groups Within 5Addition Within 10Addition Within 20Two-Digit Addition Without RegroupingTwo-Digit Addition with RegroupingAddition Within 100Repeated Addition as MultiplicationMultiplication Facts Within 100Division as Equal SharingDivision as Grouping (Measurement Division)Division: Grouping (Repeated Subtraction) ModelDivision: Fair Sharing ModelDivision as Equal SharingDivision as GroupingBasic Division FactsDivision Facts Within 100Two-Digit by One-Digit DivisionDivision with RemaindersRemainders and Quotients in DivisionDivision Word ProblemsIntroduction to Long DivisionFactors and MultiplesPrime and Composite NumbersEquivalent FractionsRelating Fractions and DecimalsDecimal Place ValueReading and Writing DecimalsComparing and Ordering DecimalsAdding and Subtracting DecimalsMultiplying DecimalsDividing DecimalsDividing FractionsMixed Number ArithmeticOrder of OperationsInteger Order of OperationsVariable ExpressionsCombining Like TermsOne-Step EquationsTwo-Step EquationsSolving Multi-Step EquationsEquations with Variables on Both SidesAngle Pairs: Complementary, Supplementary, and VerticalParallel Lines and TransversalsCorresponding AnglesAlternate Interior AnglesTriangle Angle Sum TheoremExterior Angle TheoremTriangle Inequality TheoremSimilar Triangles: AA SimilaritySimilar Triangles: SSS and SAS SimilarityProportions in Similar TrianglesRight Triangle Trigonometry IntroductionTrigonometric Ratios ReviewRadian MeasureConverting Between Degrees and RadiansThe Unit CircleGraphing Sine and CosineGraphing Tangent and Reciprocal Trigonometric FunctionsDerivatives of Trigonometric FunctionsAntiderivativesIterated Integrals and Fubini's TheoremDouble Integrals in Cartesian CoordinatesDouble Integrals over Rectangular RegionsDouble Integrals in Polar CoordinatesDouble Integrals: Definition and SetupIterated Integrals and Fubini's TheoremDouble Integrals over Rectangular RegionsDouble Integrals over General RegionsApplications of Double Integrals: Area, Mass, and MomentsTriple Integrals in Cartesian CoordinatesTriple Integrals in Cylindrical and Spherical CoordinatesChange of Variables and the Jacobian DeterminantApplications of Triple Integrals: Volume and MassVector Fields and Their RepresentationsLine Integrals of Vector FieldsGreen's TheoremSurface Integrals and Flux of Vector FieldsSurface Integrals and Flux of Vector FieldsDivergence Theorem: Flux and OutflowDivergence TheoremElectric FluxGauss's LawConductors in Electrostatic EquilibriumCapacitance and CapacitorsDielectricsDielectric Constant and Relative PermittivityElectric Field Inside Dielectric MaterialsDielectric Materials and PolarizationDielectric Susceptibility and PermittivityEnergy Density in Electric FieldsElectric Current and Current DensityElectrical Resistance and ResistivityOhm's Law and Circuit ElementsElectromotive Force (EMF) and BatteriesKirchhoff's Circuit Laws: Voltage and CurrentDC Circuit Network Analysis MethodsTransient Response in RC CircuitsRC CircuitsLC and RLC CircuitsAC Circuits: FundamentalsImpedance and ReactanceAC Power and ResonanceElectromagnetic WavesThe Electromagnetic SpectrumElectromagnetic Spectrum for Remote SensingPassive vs Active Remote SensorsOptical Remote SensingImage Preprocessing for Remote Sensing

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