Skip to main content

Photogrammetry


Photogrammetry is the science and technology of obtaining accurate measurements, maps, and 3D models from photographs.

In simple words, it is the process of using photographs to measure the size, shape, height, and location of objects on the Earth's surface.

Definition

Photogrammetry = Photo (Light) + Grammetry (Measurement)

It converts 2-dimensional (2D) photographs into 3-dimensional (3D) spatial information.

Example

If several photographs of a building are taken from different angles, photogrammetry can calculate:

  • Height of the building

  • Width and length

  • Exact geographic location

  • Complete 3D model

This technique is widely used in:

  • Topographic mapping

  • GIS

  • Remote sensing

  • Urban planning

  • Engineering

  • Archaeology

  • Agriculture

  • Forestry

  • Disaster management

Development

Photogrammetry has evolved through three major stages.

1. Analog Photogrammetry (19th Century–1960s)

This was the earliest stage.

Characteristics

  • Used film cameras

  • Images were printed on photographic film

  • Measurements were made manually

  • Optical instruments called stereoplotters were used

Working

Two overlapping photographs were viewed together to create a 3D effect.

Advantages

  • Good mapping accuracy

  • Reliable

Limitations

  • Slow

  • Time-consuming

  • Manual calculations

  • Expensive equipment

2. Analytical Photogrammetry (1960s–1990s)

Computers began replacing manual calculations.

Characteristics

  • Film photographs were still used.

  • Computers calculated object coordinates.

  • Mathematical models improved accuracy.

New Concepts

  • Coordinate computation

  • Bundle adjustment

  • Space intersection

  • Camera calibration

Advantages

  • Higher accuracy

  • Faster processing

  • Less human error

3. Digital Photogrammetry (Present)

Modern photogrammetry uses digital cameras, drones, satellites, and powerful software.

Characteristics

  • Digital images

  • Automatic image matching

  • Computer-generated 3D models

  • Orthophotos

  • Digital Elevation Models (DEM)

Modern Technologies

  • UAV (Drone)

  • Structure-from-Motion (SfM)

  • Artificial Intelligence

  • Machine Learning

  • LiDAR integration

Applications

  • Smart cities

  • Precision agriculture

  • Forest inventory

  • Construction monitoring

  • Digital twins

Principle

The fundamental principle is Triangulation.

What is Triangulation?

Triangulation means determining the position of an object by observing it from two or more different locations.

Imagine two people standing apart and looking at the same tree.

The lines of sight from both people intersect at the tree.

This intersection determines the exact location of the tree.

Photogrammetry follows the same concept.

Requirements

  • Minimum two overlapping photographs

  • Known camera positions

  • Camera geometry

Stereoscopic Vision

Humans have two eyes.

Each eye sees a slightly different image.

The brain combines these images to produce depth perception.

Photogrammetry imitates this natural process.

When two overlapping aerial photographs are viewed together,
the terrain appears three-dimensional.

This is called stereoscopic viewing.

Importance

  • Measures height

  • Detects terrain slope

  • Creates Digital Elevation Models (DEM)

Elements of Orientation

To accurately locate objects, photogrammetry requires knowledge of camera orientation.

There are two types.

1. Exterior Orientation

Exterior orientation describes where the camera was and how it was tilted when the photograph was taken.

It consists of six parameters.

Position Coordinates

  • X → East-West position

  • Y → North-South position

  • Z → Height above ground

Rotation Angles

  • ω (Omega) → Rotation about X-axis (Roll)

  • φ (Phi) → Rotation about Y-axis (Pitch)

  • κ (Kappa) → Rotation about Z-axis (Yaw)

These parameters determine the exact position and direction of the camera.

2. Interior Orientation

Interior orientation defines the camera's internal characteristics.

Important parameters include:

Focal Length (f)

Distance between the camera lens and image sensor.

Principal Point

The center of the photograph.

Lens Distortion

Imperfections of the lens causing slight image deformation.

These values are determined during camera calibration.

Types of Aerial Photographs

1. Vertical Photograph

Camera points almost straight downward.

Tilt is less than 3°.

Characteristics

  • Uniform scale

  • Minimum distortion

  • Best for mapping

Applications

  • Topographic maps

  • GIS

  • Land use mapping

2. Tilted Photograph

Camera is unintentionally tilted due to aircraft movement.

Characteristics

  • Slight distortion

  • Uneven scale

Usually avoided in accurate mapping.

3. Oblique Photograph

Camera is intentionally tilted.

Types

Low Oblique

  • Horizon is not visible

  • Larger ground coverage

High Oblique

  • Horizon is visible

  • Gives a realistic landscape view

Applications

  • Tourism

  • Military

  • Landscape studies

Scale of Aerial Photograph

Scale represents the relationship between distances on the photograph and actual ground distances.

Formula

Scale = Photo Distance / Ground Distance

Large Scale

Example

1 : 5,000

Characteristics

  • More detail

  • Small area covered

Applications

  • City mapping

  • Engineering

Small Scale

Example

1 : 100,000

Characteristics

  • Less detail

  • Large area covered

Applications

  • Regional planning

  • National mapping

Scale Variation

The scale is not always constant.

Reasons include:

Flying Height Variation

Aircraft altitude changes.

Terrain Relief

Mountains appear larger than valleys because they are closer to the camera.

This effect is called relief displacement.

Resolution

Resolution indicates how much detail an image can show.

1. Spatial Resolution

Measures the smallest object visible.

Expressed using Ground Sample Distance (GSD).

Example

5 cm GSD

Each pixel represents 5 cm on the ground.

Higher spatial resolution means finer detail.

2. Radiometric Resolution

Measures how well the sensor records differences in brightness.

Example

8-bit image

= 256 brightness levels

16-bit image

= 65,536 brightness levels

Higher radiometric resolution detects subtle variations.

3. Spectral Resolution

Measures the ability to distinguish different wavelengths.

Examples

  • Blue

  • Green

  • Red

  • Near Infrared (NIR)

Higher spectral resolution helps identify vegetation, water, soil, and minerals.

Photographic Films

Traditional aerial cameras used 23 × 23 cm film.

Panchromatic Film

Sensitive to the entire visible spectrum.

Produces black-and-white images.

Uses

  • Topographic mapping

  • Engineering surveys

Color Film

Records natural colors.

Uses

  • Land use mapping

  • Urban studies

Color Infrared (CIR)

Sensitive to Near Infrared radiation.

Healthy vegetation reflects large amounts of NIR and appears bright red.

Applications

  • Agriculture

  • Forestry

  • Wetlands

  • Crop monitoring

Filters

Filters are placed in front of the camera lens to improve image quality.

Haze Filter

Removes scattered blue light.

Improves image clarity.

Useful during hazy weather.

Band-pass Filter

Allows only selected wavelengths to pass.

Used in infrared photography.

Improves vegetation analysis.

Neutral Density (ND) Filter

Reduces the amount of light entering the camera.

Does not change colors.

Prevents overexposure in bright sunlight.

Aerial Cameras

Special cameras are mounted on aircraft or drones.

Metric Camera

A highly accurate mapping camera.

Characteristics

  • Calibrated lens

  • Very low distortion

  • Known focal length

  • High geometric accuracy

Used for

  • Topographic mapping

  • Engineering surveys

  • Cadastral mapping

Digital Aerial Camera

Modern replacement for film cameras.

Uses CCD or CMOS sensors.

Characteristics

  • High-resolution images

  • Direct digital storage

  • Faster processing

  • Multispectral imaging (RGB + NIR)

  • Easy integration with GIS and Remote Sensing software

Applications

  • Drone surveys

  • Urban planning

  • Precision agriculture

  • Disaster assessment

  • 3D city modeling


Terminology Meaning
Photogrammetry Measurement using photographs
Triangulation Determining location using intersecting lines of sight from multiple images
Stereo Pair Two overlapping photographs viewed in 3D
Overlap Common area shared between adjacent photographs
Exterior Orientation Camera position (X, Y, Z) and rotation (ω, φ, κ) during image capture
Interior Orientation Camera's internal geometry (focal length, principal point, lens distortion)
Vertical Photograph Camera points nearly straight downward
Oblique Photograph Camera intentionally tilted
Scale Ratio of photo distance to ground distance
Relief Displacement Apparent shift of tall objects due to elevation differences
Spatial Resolution Smallest object that can be distinguished
Radiometric Resolution Sensor's ability to detect brightness differences
Spectral Resolution Ability to distinguish different wavelength bands
Ground Sample Distance (GSD) Ground area represented by one image pixel
Panchromatic Film Black-and-white film sensitive to the visible spectrum
Color Infrared (CIR) Film or sensor sensitive to near-infrared for vegetation analysis
Metric Camera Calibrated camera designed for accurate mapping
Orthophoto Geometrically corrected aerial photograph with uniform scale
Structure-from-Motion (SfM) Computer vision technique that reconstructs 3D models from overlapping photographs
Uploaded Image

Comments

Post a Comment

Popular posts from this blog

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

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...
Post a Comment
Read more

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process :    - The algorithm starts by divid...
Post a Comment
Read more

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...
Post a Comment
Read more

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...
Post a Comment
Read more