Skip to main content

Geometric Correction


Geometric Correction:


- Geometric correction is a critical process in remote sensing and digital image processing. It involves adjusting and aligning an image so that it accurately represents the Earth's surface in terms of scale, orientation, and spatial accuracy. This correction compensates for various geometric distortions and errors introduced during image acquisition and sensor characteristics, ensuring that the image can be used for precise geospatial analysis and mapping.


Source of Geometric Error:


- Geometric errors in remote sensing arise from various sources, including inaccuracies in sensor characteristics, platform movement, Earth's curvature, terrain relief, atmospheric conditions, and other factors. These errors can lead to distortions, misalignments, and inaccuracies in the positioning and representation of objects within an image.


Types of Geometric Error:


- Geometric errors can manifest in different ways, including:

  1. Scale Error: Inaccurate representation of distances in the image.

  2. Positional Error: Errors in the location of objects within the image.

  3. Angular Error: Errors in the orientation or rotation of objects.

  4. Distortion: Misrepresentation of object shapes or sizes.

  5. Parallax Error: Discrepancies in object positions due to elevation differences.

  6. Relief Displacement: Displacements of objects due to variations in terrain elevation.

  7. Atmospheric Refraction: Errors due to the bending of light in the atmosphere.

  8. Satellite Ephemeris Errors: Errors in satellite position data.

  9. DEM Errors: Inaccuracies in the Digital Elevation Model used for terrain correction.

  10. Time-Dependent Errors: Errors that change over time.

  11. Resampling Error: Errors introduced during pixel value interpolation.

  12. Control Point Error: Errors in the accuracy of ground control points.


Types of Geometric Correction:


- Geometric correction techniques are used to rectify or mitigate these errors. Common types include:

  1. Image-to-Map Transformation: Matching control points to align the image with a map.

  2. Rubber Sheet Transformation: Non-linear correction using polynomial functions.

  3. Affine Transformation: Linear correction for basic distortions.

  4. Projective Transformation (Homography): Correcting complex distortions, including perspective.

  5. Orthorectification: Comprehensive correction accounting for terrain and Earth's curvature.

  6. Bundle Adjustment: Simultaneous adjustment of multiple images for 3D mapping.

  7. Sensor Model-Based Correction: Using detailed sensor models for correction.

  8. Resampling: Interpolating pixel values after correction.


Each type of geometric correction is chosen based on the specific nature of the errors in the imagery and the desired level of accuracy for the application at hand.

Comments

Post a Comment

Popular posts from this blog

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 va...
Post a Comment
Read more

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. Soluti...
Post a Comment
Read more

Radar Sensors in Remote Sensing

Radar sensors are active remote sensing instruments that use microwave radiation to detect and measure Earth's surface features. They transmit their own energy (radio waves) toward the Earth and record the backscattered signal that returns to the sensor. Since they do not depend on sunlight, radar systems can collect data: day or night through clouds, fog, smoke, and rain in all weather conditions This makes radar extremely useful for Earth observation. 1. Active Sensor A radar sensor produces and transmits its own microwaves. This is different from optical and thermal sensors, which depend on sunlight or emitted heat. 2. Microwave Region Radar operates in the microwave region of the electromagnetic spectrum , typically from 1 mm to 1 m wavelength. Common radar frequency bands: P-band (70 cm) L-band (23 cm) S-band (9 cm) C-band (5.6 cm) X-band (3 cm) Each band penetrates and interacts with surfaces differently: Lo...
Post a Comment
Read more

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...
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