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

Radar Remote Sensing SAR

1. Radar and Radar Remote Sensing RADAR stands for Radio Detection and Ranging . It is an active remote sensing system that transmits microwave energy toward the Earth's surface and records the energy that is returned to the sensor as an echo or backscatter . Unlike passive optical remote sensing, radar does not depend on sunlight. Therefore, it can operate day and night and, at suitable wavelengths, can acquire data through clouds, haze and light rain. Hence, radar is widely described as an all-weather, day-and-night remote sensing technology . Basic principle Microwave pulse → transmission → interaction with surface → backscatter/echo → antenna receives signal → signal processing → radar image 2. Microwave Energy Radar systems use microwave electromagnetic radiation , generally in wavelength ranges from approximately 1 mm to 1 m . Important radar bands include: Band Approx. wavelength Common applications ...
Post a Comment
Read more

Atmospheric Window

The atmospheric window in remote sensing refers to specific wavelength ranges within the electromagnetic spectrum that can pass through the Earth's atmosphere relatively unimpeded. These windows are crucial for remote sensing applications because they allow us to observe the Earth's surface and atmosphere without significant interference from the atmosphere's constituents. Key facts and concepts about atmospheric windows: Visible and Near-Infrared (VNIR) window: This window encompasses wavelengths from approximately 0. 4 to 1. 0 micrometers. It is ideal for observing vegetation, water bodies, and land cover types. Shortwave Infrared (SWIR) window: This window covers wavelengths from approximately 1. 0 to 3. 0 micrometers. It is particularly useful for detecting minerals, water content, and vegetation health. Mid-Infrared (MIR) window: This window spans wavelengths from approximately 3. 0 to 8. 0 micrometers. It is valuable for identifying various materials, incl...
Post a Comment
Read more

Lidar

LiDAR (Light Detection and Ranging) is an active remote sensing technology that measures distances by illuminating a target with laser pulses and analyzing the time it takes for the reflected light to return. Unlike passive systems (e.g., cameras, multispectral sensors), LiDAR provides its own energy source (laser), allowing it to operate both day and night and even penetrate through vegetation canopies . 🔹 How LiDAR Works (Step-by-Step Process) Laser Pulse Emission The system emits rapid, short pulses of laser light (commonly in the near-infrared wavelength, 1064 nm ). Some systems emit up to hundreds of thousands of pulses per second . Interaction with Target Surface The laser beam strikes objects such as vegetation, buildings, or bare ground. Depending on the object's structure, part of the pulse may scatter or reflect. Return Signal Detection The sensor records multiple returns : First Return → typically vegetation canopy tops. ...
Post a Comment
Read more

Radar image. Polarization in Remote Sensing

L band radars operate on a wavelength of 15-30 cm and a frequency of 1-2 GHz. L band radars are mostly used for clear air turbulence studies. S band radars operate on a wavelength of 8-15 cm and a frequency of 2-4 GHz. Because of the wavelength and frequency, S band radars are not easily attenuated. . Polarization refers to the direction of travel of an electromagnetic wave vector's tip: vertical (up and down), horizontal (left to right), or  circular (rotating in a constant plane left or right). . a synthetic aperture radar (SAR) for high-resolution imaging. a radar altimeter, to measure the ocean topography. echo amplitude a wind scatterometer to measure wind speed and direction. Other types of radars have been flown for Earth observation missions: precipitation radars such as the  Tropical Rainfall Measuring Mission, or cloud radars like the one used on Cloudsat. . RISAT-1 (SAR, ISRO India, 2012) RORSAT (SAR, Soviet Union, 1967-1988) Seasat (SAR, altimeter, scatterometer, US, 19...
Post a Comment
Read more

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