Skip to main content

Elements of Image Interpretation



When an analyst looks at an aerial photo or satellite image, they rely on visual interpretation keys to identify features. These include size, shape, shadows, tone, texture, pattern, association, and site context.

1. Size

  • Definition: The actual or relative dimensions of an object in the image.

  • Concept: By knowing the scale of the photo, the real-world size of features can be estimated.

  • Examples:

    • An airport runway (large and long) vs. a village road (short and narrow).

    • Comparing cars (small) with buses (larger).

  • Fact: Size alone is not enough, but it helps eliminate confusion between features.

2. Shape

  • Definition: The geometric form or outline of an object.

  • Concept: Many cultural (man-made) features have regular shapes (rectangles, circles, straight lines), while natural features are often irregular.

  • Examples:

    • Rectangular → buildings, fields.

    • Circular → water tanks, ponds, stadiums.

    • Irregular → rivers, forests.

3. Shadows

  • Definition: Dark areas cast by elevated objects when sunlight is at an angle.

  • Concept: Shadows provide information about the height, profile, and shape of objects.

  • Examples:

    • Tall buildings cast long shadows.

    • Trees can be identified by their crown shape and shadow.

  • Fact: Shadow length varies with time of day and season.

4. Tone (or Color in multispectral images)

  • Definition: The relative brightness or darkness of features, usually in gray scale (black, white, shades of gray) or color.

  • Concept: Different materials reflect light differently → gives distinctive tones.

  • Examples:

    • Water → dark tone.

    • Vegetation → medium to dark gray (healthy vegetation looks dark in infrared).

    • Sand or concrete → bright tone.

  • Fact: In multispectral imagery, tones are called spectral signatures.

5. Texture

  • Definition: The visual impression of surface roughness or smoothness.

  • Concept: Caused by the variation of tones within a small area.

  • Examples:

    • Rough texture → forests, urban areas.

    • Smooth texture → water bodies, grasslands, roads.

6. Pattern

  • Definition: The spatial arrangement of objects in the landscape.

  • Concept: Features often occur in recognizable arrangements.

  • Examples:

    • Parallel → crop fields, orchards, railway tracks.

    • Radial → road networks around a central city.

    • Grid pattern → urban planning with rectangular streets.

7. Association

  • Definition: The relationship of one feature with others nearby.

  • Concept: Certain features are commonly found together, helping identification.

  • Examples:

    • A school → sports field, playground, residential areas.

    • Railway station → railway tracks, warehouses, roads.

    • River → sand bars, floodplains, vegetation.

8. Site Context

  • Definition: The location of a feature in relation to its surroundings.

  • Concept: Position helps confirm identity of features.

  • Examples:

    • A reservoir is usually near a dam or river.

    • A lighthouse is near the coastline.

    • Farmlands are generally located in plains, not mountain tops.


  • Size → small vs. large objects.

  • Shape → geometric outline (rectangular, circular, irregular).

  • Shadows → indicate height/shape.

  • Tone → brightness/darkness (spectral signature).

  • Texture → roughness/smoothness.

  • Pattern → arrangement (linear, grid, radial).

  • Association → features found together.

  • Site context → surroundings/location clues.

👉 By combining these elements, analysts interpret natural features (rivers, forests, mountains) and cultural features (buildings, roads, cities) in aerial and satellite imagery.


Comments

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...

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...

Hazard Mapping Spatial Planning Evacuation Planning GIS

Geographic Information Systems (GIS) play a pivotal role in disaster management by providing the tools and frameworks necessary for effective hazard mapping, spatial planning, and evacuation planning. These concepts are integral for understanding disaster risks, preparing for potential hazards, and ensuring that resources are efficiently allocated during and after a disaster. 1. Hazard Mapping: Concept: Hazard mapping involves the process of identifying, assessing, and visually representing the geographical areas that are at risk of certain natural or human-made hazards. Hazard maps display the probability, intensity, and potential impact of specific hazards (e.g., floods, earthquakes, hurricanes, landslides) within a given area. Terminologies: Hazard Zone: An area identified as being vulnerable to a particular hazard (e.g., flood zones, seismic zones). Hazard Risk: The likelihood of a disaster occurring in a specific location, influenced by factors like geography, climate, an...

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...