Vineesh V, Geography

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Pluvial Fluvial Nival

Supervised classification is a digital image classification method where the analyst guides the classification process by defining classes of interest and providing representative training samples. The classifier uses these training samples to learn the spectral signatures of each class and then assigns every pixel in the image to the most appropriate class. This method relies heavily on prior knowledge of the study area. How Supervised Classification Works ✔ Step 1: Define Information Classes These are real-world land-cover classes such as: water forest agriculture urban barren land ✔ Step 2: Select Training Areas Training areas (also called ROIs—Regions of Interest) are chosen on the image where the analyst is confident about the land-cover type. ✔ Step 3: Extract Spectral Signatures The classifier calculates: mean variance covariance pixel distribution for each class across different spectral bands. ✔ Step 4: Apply ...

Atmosphere