Image Classification Techniques in Remote Sensing

Image Classification

Image Classification is the process of assigning individual pixels or groups of pixels in a remotely sensed image to specific land-cover or land-use categories based on their spectral characteristics. The objective is to convert image data into meaningful thematic information such as forests, water bodies, agricultural land, urban areas, and barren land.

Objectives

• Identify different land-cover types.
• Monitor environmental changes.
• Support resource management.
• Assist in urban and regional planning.
• Analyze agricultural and forest conditions.

Types of Classification

1. Supervised Classification
2. Unsupervised Classification

Supervised Classification

Definition

Supervised classification is a classification method in which the analyst provides sample areas (training sites) for known land-cover classes. The software uses these samples to classify the remaining pixels in the image. The analyst "supervises" the classification process by identifying representative examples of each class.

Procedure of Supervised Classification

1. Selection of Training Areas: The user identifies representative samples of known classes such as water, forest, urban area, agricultural land, and barren land.
2. Creation of Spectral Signatures: The software analyzes the spectral characteristics of the training samples and develops statistical descriptions called spectral signatures.
3. Classification: Each pixel in the image is compared with the spectral signatures and assigned to the most appropriate class.
4. Accuracy Assessment: The classified image is checked against ground truth data to evaluate classification accuracy.

Common Supervised Classification Algorithms

• Minimum Distance Classifier
• Maximum Likelihood Classifier
• Random Forest Classifier

Advantages

• Generally more accurate.
• Classes are defined according to study objectives.
• Better control over classification results.
• Suitable when prior knowledge of the area exists.

Disadvantages

• Requires extensive field knowledge.
• Time-consuming selection of training samples.
• Results depend heavily on the quality of training data.
• Spectral overlap may reduce accuracy.

Applications

• Land-use mapping
• Forest inventory
• Crop monitoring
• Urban growth studies
• Wetland mapping

Unsupervised Classification

Unsupervised classification is a method in which the computer automatically groups pixels into clusters based on their spectral similarity without prior information about land-cover classes. The analyst interprets and labels the clusters after classification.

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