Define Supervised image classification.

Supervised image classification is a process in remote sensing and digital image analysis where a computer algorithm categorizes pixels or groups of pixels within an image based on training samples provided by the user. Unlike unsupervised classification, where the algorithm identifies patterns without prior knowledge, supervised classification relies on a predefined set of classes and known examples to guide the classification process.

Key Components of Supervised Image Classification:

1. Training Samples:
   Users select representative samples, also known as training samples or training pixels, from the image that correspond to specific land cover or land use classes. These samples serve as examples for the algorithm to learn the spectral characteristics associated with each class.

2. Training Areas:
   Training areas are regions within the image where the selected training samples are located. These areas provide the algorithm with spatial context and help in capturing variations within each class. It's important to ensure that the training areas are representative of the entire class.

3. Feature Extraction:
   Feature extraction involves identifying spectral, textural, or spatial characteristics of the training samples. The algorithm uses these features to discriminate between different classes during the classification process. Common features include reflectance values from different spectral bands, texture patterns, and contextual information.

4. Classifier Algorithm:
   A classifier algorithm is trained using the selected training samples and their associated features. Popular classifiers include maximum likelihood, support vector machines, decision trees, and neural networks. The classifier learns to distinguish between classes based on the feature space defined by the training samples.

5. Validation and Accuracy Assessment:
   Once the classification is performed, the results need to be validated and assessed for accuracy. This is done by comparing the classified image with independently collected reference data. Accuracy assessment metrics, such as overall accuracy and kappa coefficient, quantify the reliability of the classification.

6. Classified Image:
   The final output of supervised classification is a classified image where pixels are assigned to specific land cover or land use classes based on the learned characteristics from the training samples. Each pixel in the image is assigned a class label, providing a spatial representation of the different features on the ground.

Applications of Supervised Image Classification:

1. Land Cover Mapping:
   Supervised classification is widely used for mapping and monitoring land cover types, including forests, agricultural fields, urban areas, and water bodies.

2. Change Detection:
   By comparing classified images from different time periods, supervised classification supports change detection analysis, identifying alterations in land cover over time.

3. Resource Management:
   In applications like agriculture and forestry, supervised classification aids in assessing crop health, estimating vegetation biomass, and monitoring deforestation.

4. Urban Planning:
   Supervised classification helps in urban planning by delineating and categorizing different urban features, such as buildings, roads, and parks.

5. Environmental Monitoring:
   Applications in environmental science include monitoring ecosystems, assessing habitat changes, and studying the impact of natural disasters.

Supervised image classification is a powerful tool for extracting valuable information from remote sensing data, contributing to a wide range of applications in resource management, environmental monitoring, and land use planning.