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

CREATION OF SPATIAL DATA

Spatial data creation is the process of generating, organizing, and managing geographically referenced information in a Geographic Information System (GIS). It involves converting maps, satellite images, GPS observations, and field survey data into digital datasets that can be stored, analyzed, and visualized. The quality of GIS analysis depends largely on the accuracy of spatial data creation. 1. Creation of Shapefile and Geodatabase A. Shapefile A Shapefile is one of the most widely used vector data formats developed by Esri for storing geographic features. Definition A shapefile stores the geometry and attributes of geographic features such as points, lines, and polygons. Components of a Shapefile A shapefile consists of several files: .shp – Stores geometry (shape) .shx – Shape index .dbf – Attribute table .prj – Coordinate Reference System (CRS) .sbn/.sbx – Spatial index (optional) Geometry Types Point – W...
Post a Comment
Read more

Geography of Health or Medical Geography

Health Geography (also known as Medical Geography ) is a sub-discipline of Human Geography that studies the relationships between place, environment, society, and health . It examines how spatial location, environmental conditions, and social and economic factors influence human health, disease patterns, and access to healthcare services. Health geography integrates concepts from geography, epidemiology, medicine, public health, environmental science, sociology, and Geographic Information Systems (GIS) to understand and improve population health. Major Components of Health Geography Health geography is generally divided into two major branches : The Geography of Disease and Ill Health The Geography of Health Care 1. The Geography of Disease and Ill Health This branch studies the spatial distribution, determinants, and diffusion of diseases across different geographical scales, from neighborhoods to global regions. It seeks t...
Post a Comment
Read more

Nature and Scope of Geography

Geography is the scientific study of the Earth's surface, its physical features, human populations, and the interactions between people and their environment. The word Geography is derived from the Greek words Geo (Earth) and Graphien (to describe or write), meaning "description of the Earth." Modern geography goes far beyond description; it seeks to explain where phenomena occur, why they occur there, how they are spatially distributed, and how they change over time. Geography is regarded as a spatial science , an environmental science , and an integrative discipline because it bridges natural sciences, social sciences, and geospatial technologies. Nature The nature of geography refers to the characteristics and fundamental features that define the discipline. 1. Geography as a Spatial Science Terminology: Spatial Science A discipline concerned with the location, distribution, arrangement, organization, and interaction of phenomena in ...
Post a Comment
Read more

Historical Development of Geography in the Ancient Period

The Ancient Period marks the earliest stage in the evolution of geographical thought, extending from approximately 3000 BCE to the 5th century CE . During this period, geography evolved from simple descriptions of the Earth's surface to systematic scientific inquiry. Early civilizations developed geographical knowledge to meet practical needs such as navigation, trade, agriculture, military expansion, taxation, and administration . The greatest contributions came from the Mesopotamian, Egyptian, Indian, Chinese, Greek, and Roman civilizations , with the Greeks laying the foundations of scientific geography . Meaning Terminology: Historical Development Historical development refers to the gradual evolution of geographical knowledge, concepts, methods, and theories over time. Concept Geographical knowledge evolved through: Observation of the natural environment Exploration and travel Cartography (map-making) Astronomical observations ...
Post a Comment
Read more

Remote Sensing: Energy Sources, Wave Model of Electromagnetic Energy, and Quantum Theory of Electromagnetic Radiation

Remote sensing is the science of collecting information about the Earth's surface without physically touching it . It works by detecting and measuring electromagnetic radiation (EMR) that is emitted or reflected by objects. 1. Energy Sources What is an Energy Source? An energy source is anything that produces electromagnetic radiation (EMR). Without energy, remote sensing cannot detect objects. Definition Energy Source: The origin of electromagnetic energy that illuminates or is emitted by an object so that a sensor can detect it. Types of Energy Sources A. Natural Energy Source (Passive Remote Sensing) The Sun is the most important natural energy source. Produces visible light, infrared, and ultraviolet radiation. Sunlight travels through space and reaches the Earth. Objects absorb part of this energy and reflect the remaining energy. Satellites measure this reflected energy. Examples ...
Post a Comment
Read more