Skip to main content

Geometric Correction



When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation.
These distortions make the image not properly aligned with real-world coordinates (latitude and longitude).

👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface.

After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data.

Types 

1. Systematic Correction

Systematic errors are predictable and can be modeled mathematically.
They occur due to the geometry and movement of the satellite sensor or the Earth.

Common systematic distortions:

  • Scan skew – due to the motion of the sensor as it scans the Earth.

  • Mirror velocity variation – scanning mirror moves at a variable speed.

  • Cross-track distortion – image stretching across the scan direction.

  • Earth rotation skew – Earth rotates while the sensor scans, shifting positions.

  • Platform altitude variation – changes in satellite height.

  • Platform velocity variation – changes in satellite speed.

Correction method:
Systematic errors are corrected using mathematical models or formulas derived from satellite geometry and sensor parameters.
This process is often automated and is part of orthorectification, which adjusts images for terrain relief using a Digital Elevation Model (DEM).

2. Non-Systematic Correction

Non-systematic (random) errors are unpredictable — caused by sensor drift, attitude changes, or human error.
They cannot be fixed mathematically and require ground reference points.

It involves aligning image coordinates with real-world coordinates or another image.

Two main approaches:

(a) Image-to-Ground Correction (Georeferencing)

  • The image is aligned to real-world ground coordinates (latitude/longitude).

  • Requires Ground Control Points (GCPs)—known locations visible on both the image and a map.

(b) Image-to-Image Correction (Registration)

  • Used when two or more images of the same area (different times/sensors) must match perfectly.

  • One image acts as the reference, and the other is adjusted to match it.

Coordinate Transformation

This step mathematically links image coordinates (rows and columns) to map coordinates (X, Y).

A polynomial transformation is used, where the order of the polynomial defines the complexity of the correction.


👉 Examples:

  • 1st order (affine): needs 3 GCPs → corrects translation, rotation, scaling, and skew.

  • 2nd order: needs 6 GCPs → can correct moderate curvilinear distortions.

  • 3rd order: needs 10 GCPs → handles more complex distortions.

Accuracy Assessment:

Accuracy of geometric correction can be measured by Root Mean Square Error (RMSE):

[
RMSE = \sqrt{\frac{(D_1^2 + D_2^2 + D_3^2 + ... + D_n^2)}{n}}
]

Where D = distance between the corrected pixel and its true location.
A smaller RMSE means higher geometric accuracy.

Resampling

When an image is geometrically corrected or transformed, the pixel grid changes.
Resampling determines what new pixel values to assign in the corrected image.

In simple words:
It's the process of fitting old pixels into a new coordinate grid after correction.

Because the input and output grids rarely match exactly, resampling decides which value each new pixel should take.

Common Resampling Methods:

  1. Nearest Neighbour (NN):

    • Takes the value of the closest original pixel.

    • Simple and fast.

    • Best for categorical data (like land use classes).

    • May look blocky.

  2. Bilinear Interpolation:

    • Uses the average of 4 nearest pixels.

    • Produces smoother images.

    • Suitable for continuous data (like temperature, elevation).

  3. Cubic Convolution:

    • Uses 16 nearest pixels with weighted averages.

    • Produces very smooth and visually appealing images.

    • Best for display and analysis of continuous data.




Miscellaneous Pre-Processing Steps

1. Subsetting

Selecting or cutting out a smaller portion of a large image (based on AOI – Area of Interest).

  • Helps reduce file size.

  • Makes processing faster.
    Example: Cropping a satellite image to only your study district.

2. Mosaicking

Combining two or more overlapping satellite images to form one continuous image covering a larger area.

  • Useful when one scene doesn't cover the full study region.

  • Must ensure brightness matching between scenes.


StepPurposeExample / Key Point
Geometric CorrectionAlign image with real-world coordinatesCorrects distortions
Systematic CorrectionFix predictable errorsUses sensor models, orthorectification
Non-Systematic CorrectionFix random errorsUses GCPs for georeferencing
Coordinate TransformationConverts pixel to map coordinatesUses polynomial equations
ResamplingAssigns pixel values in new gridNN, Bilinear, Cubic methods
SubsettingExtracts part of an imageFocus on study area
MosaickingCombines multiple scenesCreates larger continuous image


Comments

Post a Comment

Popular posts from this blog

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...
Post a Comment
Read more

GIS data continuous discrete ordinal interval ratio

In Geographic Information Systems (GIS) , data is categorized based on its nature (discrete or continuous) and its measurement scale (nominal, ordinal, interval, or ratio). These distinctions influence how the data is collected, analyzed, and visualized. Let's break down these categories with concepts, terminologies, and examples: 1. Discrete Data Discrete data is obtained by counting distinct items or entities. Values are finite and cannot be infinitely subdivided. Characteristics : Represent distinct objects or occurrences. Commonly represented as vector data (points, lines, polygons). Values within a range are whole numbers or categories. Examples : Number of People : Counting individuals on a train or in a hospital. Building Types : Categorizing buildings as residential, commercial, or industrial. Tree Count : Number of trees in a specific area. 2. Continuous Data Continuous data is obtained by measuring phenomena that can take any value within a range...
Post a Comment
Read more

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology:    - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants.    - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis:    - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis.    - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping:    - Photozincography development allows maps to be split into layers for vegetation, water, etc.    - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography:    - Waldo Tobler's 1959 publication details using computers for cartography.    - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...
Post a Comment
Read more

Platforms in Remote Sensing

In remote sensing, a platform is the physical structure or vehicle that carries a sensor (camera, scanner, radar, etc.) to observe and collect information about the Earth's surface. Platforms are classified mainly by their altitude and mobility : Ground-Based Platforms Definition : Sensors mounted on the Earth's surface or very close to it. Examples : Tripods, towers, ground vehicles, handheld instruments. Applications : Calibration and validation of satellite data Detailed local studies (e.g., soil properties, vegetation health, air quality) Strength : High spatial detail but limited coverage. Airborne Platforms Definition : Sensors carried by aircraft, balloons, or drones (UAVs). Altitude : A few hundred meters to ~20 km. Examples : Airplanes with multispectral scanners UAVs with high-resolution cameras or LiDAR High-altitude balloons (stratospheric platforms) Applications : Local-to-regional mapping ...
Post a Comment
Read more

Disaster Management

1. Disaster Risk Analysis → Disaster Risk Reduction → Disaster Management Cycle Disaster Risk Analysis is the first step in managing disasters. It involves assessing potential hazards, identifying vulnerable populations, and estimating possible impacts. Once risks are identified, Disaster Risk Reduction (DRR) strategies come into play. DRR aims to reduce risk and enhance resilience through planning, infrastructure development, and policy enforcement. The Disaster Management Cycle then ensures a structured approach by dividing actions into pre-disaster, during-disaster, and post-disaster phases . Example Connection: Imagine a coastal city prone to cyclones: Risk Analysis identifies low-lying areas and weak infrastructure. Risk Reduction includes building seawalls, enforcing strict building codes, and training residents for emergency situations. The Disaster Management Cycle ensures ongoing preparedness, immediate response during a cyclone, and long-term recovery afterw...
Post a Comment
Read more