Photogrammetry – Types of Photographs

In photogrammetry, aerial photographs are categorized based on camera orientation, coverage, and spectral sensitivity. Below is a breakdown of the major types:

1️⃣ Based on Camera Axis Orientation

Type	Description	Key Feature
Vertical Photo	Taken with the camera axis pointing directly downward (within 3° of vertical).	Used for maps and measurements
Oblique Photo	Taken with the camera axis tilted away from vertical.	Covers more area but with distortions

Low Oblique: Horizon not visible
High Oblique: Horizon visible

2️⃣ Based on Number of Photos Taken

Type	Description
Single Photo	One image taken of an area
Stereoscopic Pair	Two overlapping photos for 3D viewing and depth analysis
Strip or Mosaic	Series of overlapping photos covering a long area, useful in mapping large regions

3️⃣ Based on Spectral Sensitivity

Type	Description	Application
Panchromatic	Captures images in black and white	General mapping
Infrared (IR)	Sensitive to infrared radiation	Vegetation, water, heat
Color	Natural color images	Urban studies, land use
Color Infrared (CIR)	Uses IR, red, and green bands	Health of vegetation, hydrology

4️⃣ Based on Lens Angle

Type	Field of View
Narrow-angle	Small area, high detail
Normal-angle	Medium coverage
Wide-angle	Large area, less detail
Super wide-angle / Fisheye	Very large area, with extreme distortion

5️⃣ Based on Time and Platform

Type	Platform
Aerial Photo	Taken from aircraft or drones
Satellite Image	Taken from orbiting satellites
Terrestrial Photo	Taken from ground level (horizontal photos)

✅ Summary Table

Orientation	Number	Spectrum	Field of View	Platform
Vertical / Oblique	Single / Stereo	Panchromatic / IR / CIR	Narrow / Wide	Aerial / Satellite / Terrestrial

Comments

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...

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...

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...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

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...

Vineesh V, Geography

Search This Blog