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

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

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...
Post a Comment
Read more

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...
Post a Comment
Read more

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 cholĂ©ra dans Paris et le dĂ©partement 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

Representation of Spatial and Temporal Relationships

In GIS, spatial and temporal relationships allow the integration of location (the "where") and time (the "when") to analyze phenomena across space and time. This combination is fundamental to studying dynamic processes such as urban growth, land-use changes, or natural disasters. Key Concepts and Terminologies Geographic Coordinates : Define the position of features on Earth using latitude, longitude, or other coordinate systems. Example: A building's location can be represented as (11.6994° N, 76.0773° E). Timestamp : Represents the temporal aspect of data, such as the date or time a phenomenon was observed. Example: A landslide occurrence recorded on 30/07/2024 . Spatial and Temporal Relationships : Describes how features relate in space and time. These relationships can be: Spatial : Topological (e.g., "intersects"), directional (e.g., "north of"), or proximity-based (e.g., "near"). Temporal : Sequential (e....
Post a Comment
Read more