Skip to main content

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 classes.   - You can verify the training areas with ground truth data.   - You can identify distinct, homogeneous regions for each class.

Unsupervised Classification

Unsupervised classification, on the other hand, uses the spectral properties of the image data to automatically group pixels with similar spectral characteristics into spectral classes. These classes are later labeled by the analyst based on the spectral patterns and ground-truth information.

  • When to Use Unsupervised Classification:   - You have limited prior knowledge about the image's content.   - You need a large number of classes or wish to explore the data's spectral characteristics.   - It's beneficial for quickly exploring unknown regions.

2. Key Stages of Image Classification

Image classification follows a systematic series of stages to produce accurate thematic maps.

  1. Raw Data Collection: Initial, unprocessed image data is collected.
  2. Preprocessing: Prepares the data for analysis by correcting atmospheric effects, removing noise, and aligning geometry. This stage is essential to ensure data accuracy.
  3. Signature Collection: In supervised classification, the analyst collects samples, called signatures, representing each class. These signatures capture the typical spectral characteristics for each category.
  4. Signature Evaluation: The quality and distinctiveness of signatures are evaluated to ensure that they are statistically separate and represent the classes accurately.
  5. Classification: Using the collected signatures, the classification algorithm assigns each pixel to a specific class, producing the classified map.

3. Information Class vs. Spectral Class

  • Information Class: An information class represents real-world categories, such as water bodies, urban areas, or vegetation, specified by the analyst for extraction from the image.
  • Spectral Class: A spectral class is determined by the clustering of pixels with similar spectral (color or brightness) values. These classes are automatically identified based on statistical similarities in pixel values across multiple spectral bands.

4. Supervised vs. Unsupervised Training

To classify an image, a system needs to be trained to recognize patterns.

  • Supervised Training:   - Controlled by the analyst, who selects representative pixels and instructs the system on what each class should look like.   - Often more accurate but requires skill and understanding of the region.
  • Unsupervised Training:   - The computer automatically groups pixels based on spectral properties, with the analyst specifying the desired number of classes.   - This approach requires less skill but may be less accurate.

5. Classification Decision Rules in Supervised Classification

In supervised classification, different decision rules guide the process of assigning pixels to classes. Here are some common ones:

Parametric Decision Rules

These rules assume that pixel values follow a normal distribution, which allows the system to use statistical measures for classification.

  • Minimum Distance Classifier:   - Calculates the distance between a candidate pixel and the mean of each class signature.   - Assigns the pixel to the class with the shortest distance (e.g., Euclidean or Mahalanobis distance).
  • Maximum Likelihood Classifier:   - Considers both variance and covariance within class signatures.   - Assumes a normal distribution and assigns pixels to the class with the highest probability of belonging.

Nonparametric Decision Rules

These rules do not assume a specific distribution.

  • Parallelepiped Classifier:   - Uses minimum and maximum values for each class and assigns pixels within these limits to the corresponding class.

  • Feature Space Classifier:   - Analyzes classes based on polygons within a feature space, which is often more accurate than the parallelepiped method.

Summary Table

AspectSupervised ClassificationUnsupervised Classification
DefinitionUses predefined classes and training areas.Uses statistical groupings based on spectral properties.
ClassesInformation Classes: Known classes defined by the analyst.Spectral Classes: Classes identified by the system.
Training ProcessAnalyst selects and verifies classes.System automatically groups pixels; analyst labels classes.
Best Use CaseWhen classes are known, distinct, and verifiable with ground truth.When classes are unknown or when exploratory analysis is needed.
Accuracy and Skill RequirementHigh accuracy; requires skill and knowledge.Generally lower accuracy; requires less skill.
Decision RulesMinimum Distance, Maximum Likelihood, Parallelepiped, Feature Space.Classes grouped by spectral similarity.

https://geogisgeo.blogspot.com/2023/01/minimum-distance-gaussian-maximum.html



PG and Research Department of Geography,
Government College Chittur, Palakkad
https://g.page/vineeshvc

Comments

Post a Comment

Popular posts from this blog

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 Veget...
Post a Comment
Read more

Photogrammetry – Geometry of a Vertical Photograph

Photogrammetry is the science of making measurements from photographs, especially for mapping and surveying. When the camera axis is perpendicular (vertical) to the ground, the photo is called a vertical photograph , and its geometry is central to accurate mapping.  Elements of Vertical Photo Geometry In a vertical aerial photograph , the geometry is governed by the central projection principle. Here's how it works: 1. Principal Point (P) The point on the photo where the optical axis of the camera intersects the photo plane. It's the geometric center of the photo. 2. Nadir Point (N) The point on the ground directly below the camera at the time of exposure. Ideally, in a perfect vertical photo, the nadir and principal point coincide. 3. Photo Center (C) Usually coincides with the principal point in a vertical photo. 4. Ground Coordinates (X, Y, Z) Real-world (map) coordinates of objects photographed. 5. Flying Height (H) He...
Post a Comment
Read more

Raster Data Structure

Raster Data Raster data is like a digital photo made up of small squares called cells or pixels . Each cell shows something about that spot — like how high it is (elevation), how hot it is (temperature), or what kind of land it is (forest, water, etc.). Think of it like a graph paper where each box is colored to show what's there. Key Points What's in the cell? Each cell stores information — for example, "water" or "forest." Where is the cell? The cell's location comes from its place in the grid (like row 3, column 5). We don't need to store its exact coordinates. How Do We Decide a Cell's Value? Sometimes, one cell covers more than one thing (like part forest and part water). To choose one value , we can: Center Point: Use whatever feature is in the middle. Most Area: Use the feature that takes up the most space in the cell. Most Important: Use the most important feature (like a road or well), even if it...
Post a Comment
Read more

Logical Data Model in GIS

In GIS, a logical data model defines how data is structured and interrelated—independent of how it is physically stored or implemented. It serves as a blueprint for designing databases, focusing on the organization of entities, their attributes, and relationships, without tying them to a specific database technology. Key Features Abstraction : The logical model operates at an abstract level, emphasizing the conceptual structure of data rather than the technical details of storage or implementation. Entity-Attribute Relationships : It identifies key entities (objects or concepts) and their attributes (properties), as well as the logical relationships between them. Business Rules : Business logic is embedded in the model to enforce rules, constraints, and conditions that ensure data consistency and accuracy. Technology Independence : The logical model is platform-agnostic—it is not tied to any specific database system or storage format. Visual Representat...
Post a Comment
Read more

Photogrammetry

Photogrammetry is the science of taking measurements from photographs —especially to create maps, models, or 3D images of objects, land, or buildings. Imagine you take two pictures of a mountain from slightly different angles. Photogrammetry uses those photos to figure out the shape, size, and position of the mountain—just like our eyes do when we see in 3D! Concepts and Terminologies 1. Photograph A picture captured by a camera , either from the ground (terrestrial) or from above (aerial or drone). 2. Stereo Pair Two overlapping photos taken from different angles. When seen together, they help create a 3D effect —just like how two human eyes work. 3. Overlap To get a 3D model, photos must overlap each other: Forward overlap : Between two photos in a flight line (usually 60–70%) Side overlap : Between adjacent flight lines (usually 30–40%) 4. Scale The ratio of the photo size to real-world size. Example: A 1:10,000 scale photo means 1 cm on the photo...
Post a Comment
Read more