Skip to main content

RADIOMETRIC CORRECTION

 


Radiometric correction is the process of removing sensor and environmental errors from satellite images so that the measured brightness values (Digital Numbers or DNs) truly represent the Earth's surface reflectance or radiance.

In other words, it corrects for sensor defects, illumination differences, and atmospheric effects.

1. Detector Response Calibration

Satellite sensors use multiple detectors to scan the Earth's surface. Sometimes, each detector responds slightly differently, causing distortions in the image. Calibration adjusts all detectors to respond uniformly.

This includes:

(a) De-Striping

  • Problem: Sometimes images show light and dark vertical or horizontal stripes (banding).

    • Caused by one or more detectors drifting away from their normal calibration — they record higher or lower values than others.

    • Common in early Landsat MSS data.

  • Effect: Every few lines (e.g., every 6th line) appear consistently brighter or darker.

  • Solution (De-Striping):

    • Compare histograms of scan lines (e.g., 1,7,13 or 2,8,14) for mean and standard deviation.

    • Adjust the detector's response to match neighboring detectors.

    • Methods:

      • Histogram equalization and normalization

      • Fourier transformation (removes periodic striping patterns)

(b) Missing Scan Line Removal

  • Problem: Sometimes a detector stops working or becomes temporarily saturated, creating blank lines or missing data in the image.

  • Solution:

    • Replace missing lines with estimated pixel values based on the lines above and below using interpolation techniques.

    • Example: Affected Landsat 7 ETM+ (Scan Line Corrector failure).

(c) Random Noise Removal

  • Problem: Some pixels show random bright or dark spots known as "salt-and-pepper noise" or "snowy noise."

    • Caused by random electronic interference or transmission errors.

  • Solution:

    • Spatial filtering: Replace noisy pixels with average values from neighboring pixels.

    • Convolution filtering: Smooths image by using a moving filter (kernel) to reduce random pixel variation.

(d) Vignetting Removal

  • Problem: In images taken with lenses, the corners often appear darker than the center — this is vignetting.

  • Cause: Uneven illumination across the sensor array or lens curvature.

  • Solution:

    • Use sensor calibration data that describes how brightness varies from center to edges.

    • Apply Fourier Transform or other normalization methods to equalize brightness.

2. Sun Angle and Topographic Correction

(a) Sun Angle Correction

  • The sun's position changes with time of day and season, affecting image brightness.

  • Higher solar angle (summer) → more direct sunlight → brighter image.

  • Lower solar angle (winter) → less sunlight → darker image.

  • Correction Method:

    • Adjust each pixel's brightness (DN) by dividing it with the sine of the solar elevation angle:
      [
      DN_{corrected} = \frac{DN_{original}}{\sin(\text{solar elevation angle})}
      ]

    • Solar elevation data is given in the image metadata or header file.

(b) Topographic Correction

  • Problem: In hilly or mountainous areas, slopes facing the sun appear brighter, while those facing away appear darker due to uneven solar illumination.

  • Cause:

    • Slope and aspect of terrain

    • Shadowing effects

    • Bidirectional Reflectance Distribution Function (BRDF) differences

  • Solution: Adjust radiance based on slope orientation and sun angle using models such as:

    Minnaert Correction:
    [
    L_n = L \cdot (\cos e)^{k-1} \cdot \cos i
    ]
    Where:

    • (L_n): normalized radiance

    • (L): measured radiance

    • (e): slope angle (from DEM)

    • (i): solar incidence angle

    • (k): Minnaert constant (depends on land cover and illumination conditions)

    This correction helps produce uniform brightness across slopes.

3. Atmospheric Correction

  • Problem: Before reaching the sensor, sunlight interacts with the atmosphere, where gases, dust, and water vapor scatter and absorb radiation.

    • Causes haze, color distortion, and lower contrast in the image.

  • Goal: Remove the effects of atmosphere to obtain true surface reflectance.

  • Methods:

    • Dark Object Subtraction (DOS): Assumes that dark pixels (like water) should have near-zero reflectance; subtracts atmospheric haze values.

    • Radiative Transfer Models: e.g., 6S, MODTRAN, FLAASH, or QUAC to simulate atmospheric scattering and absorption effects accurately.


Type of CorrectionProblem FixedExample of ErrorCommon Methods
Detector CalibrationUneven sensor responseStriping, noiseHistogram matching, Fourier transform
Missing LineLost data linesLandsat 7 SLC failureInterpolation
Random NoiseSalt-and-pepper noiseBright/dark spotsSpatial/convolution filtering
VignettingDark cornersLens-based imagesFourier normalization
Sun AngleSeasonal/diurnal illuminationWinter images darkerDivide by sin(solar angle)
TopographicSlope illumination differenceBright/dark slopesMinnaert correction
AtmosphericScattering, absorptionHazy imagesDOS, FLAASH, MODTRAN


Comments

Post a Comment

Popular posts from this blog

Kuhn’s Paradigms

The given content explains Thomas S. Kuhn’s model of scientific development , its application to geography, and criticisms by Karl Popper, Paul Feyerabend, Michel Foucault , and others. 1. Basic Idea Kuhn argued that science does not develop continuously in a straight line . Instead, scientific development occurs through: Preparadigm → Paradigm → Normal Science → Crisis → Scientific Revolution → New Paradigm A new paradigm may replace an older one, producing a major change in the way scientists understand and study a subject. Concepts and Terminologies Concept / Term Simple Meaning Paradigm A commonly accepted framework/model that guides scientific research Exemplar A successful concrete problem-solution used as a model for future research Disciplinary Matrix Shared beliefs, values, concepts, methods and techniques of a scientific community Preparadig...
Post a Comment
Read more

Regional Geography, Systematic Geography, Idiographic, Nomothetic, Inductive and Deductive Approaches

T wo major ways of studying Geography : the Regional Approach and the Systematic Approach . It also explains the related ideas of idiographic vs. nomothetic and inductive vs. deductive reasoning , especially in the context of the Hartshorne–Schaefer debate . 1. Regional Geography: “All About One” Regional Geography studies one particular region in detail . A region is an area that has some degree of homogeneity (sameness) within its boundary but is also unique or different from other regions . For example, if we study Palakkad District , we may study: Relief and drainage Climate Soil Vegetation Agriculture Population Occupation Economy Culture Political characteristics The purpose is to understand the complete geographical personality of Palakkad and the relationships among its different features. Key concepts Region: A geographical area with identifiable characteristics and boundaries. Homogeneity: Si...
Post a Comment
Read more

Models and Theories in Geography

Geographical Models A model is a simplified representation of reality used to describe, explain, simulate, and predict geographical phenomena. Types Physical (Iconic) Models – Three-dimensional representations (e.g., globe, relief model). Conceptual Models – Diagrams or frameworks explaining geographical relationships. Mathematical (Quantitative) Models – Equations and statistical models for spatial analysis and prediction. Simulation Models – Computer-based models that simulate geographical processes (e.g., climate, flood, urban growth). Major Geographical Models Model Scholar Year Concept Johann Heinrich von Thünen Agricultural Land Use Model 1826 Land use varies with distance from the market. Walter Christaller Central Place Model 1933 Distribution of settlements and services. Ernest Burgess Concentric Zone M...
Post a Comment
Read more

Multispectral and Hyperspectral Imaging Systems

The main idea is how a remote-sensing sensor collects information about an area . A sensor does not simply take an ordinary photograph. It measures the electromagnetic energy reflected or emitted by objects in different wavelength bands . Depending on how many bands are measured and how the sensor collects them, different imaging systems are used. 1. Multispectral vs. Hyperspectral Multispectral imaging (MSI) records information in a limited number of relatively broad, separate spectral bands , such as blue, green, red, near-infrared and shortwave infrared. Hyperspectral imaging (HSI) records information in many narrow and usually contiguous spectral bands . Therefore, it provides a much more detailed spectral signature of each pixel. The resulting dataset is commonly called a hyperspectral data cube (hypercube) because it contains: X-axis → spatial information Y-axis → spatial information Z-axis → wavelength/spectral information Thus, hype...
Post a Comment
Read more

Kuhn’s model in Geography

Thomas Kuhn (1922–1996) Thomas Samuel Kuhn was an American philosopher and historian of science . In 1962 , he published The Structure of Scientific Revolutions , introducing the concepts of paradigm and paradigm shift , which transformed the understanding of scientific progress. Definition: A framework of assumptions, concepts, and values that guide a group or field. Example (Science): Moving from a-earth-centered universe to a sun-centered solar system is a change in the scientific paradigm. Example (Daily Life): A shared cultural belief or a standard way of doing business.   Kuhn's Model (1962) Kuhn's Model explains that science develops through successive paradigms rather than by continuous, gradual progress. Stages of Kuhn's Model Pre-paradigm Stage – No common theory; different ideas exist. Normal Science – Scientists work within an accepted paradigm . Crisis – Anom...
Post a Comment
Read more