Date: April 3rd, 2026 6:24 PM
Author: https://imgur.com/a/o2g8xYK
Supervised classification of infrared (IR) images is a technique in remote sensing and image analysis where known training samples (labeled data) are used to train an algorithm to categorize all pixels in an IR image into specific classes. This process is crucial for mapping land cover, identifying surface materials, or analyzing thermal patterns, as different materials often show distinct spectral signatures in IR bands.
Core Steps in Supervised Classification
The supervised classification process generally involves the following steps:
Identify Input Bands: Selecting the appropriate IR bands (e.g., Near-Infrared, Mid-IR, Thermal IR) from sensor data, such as Landsat 8.
Create Training Samples: Defining "Regions of Interest" (ROIs) or polygons over known areas (e.g., water, vegetation, urban areas) on the image.
Generate Signature File: The software analyzes the spectral characteristics of these pixels to create a signature file (a spectral profile for each class).
Run Classification: Applying an algorithm to classify the entire image based on the signature files.
Accuracy Assessment: Comparing the classified map against ground truth data to verify accuracy.
Common Classification Algorithms
Several algorithms are commonly used in platforms like ArcGIS Pro or QGIS:
Maximum Likelihood: Calculates the probability that a pixel belongs to a certain class.
Random Forest: An ensemble method that builds multiple decision trees for higher accuracy.
Minimum Distance: Assigns pixels to the nearest class center.
(http://www.autoadmit.com/thread.php?thread_id=5853160&forum_id=2#49791849)