What is supervised classification? Describe the various steps involved in supervised classification.

Supervised classification is a technique in remote sensing used to categorize pixels of an image into predefined classes or categories based on training data. In this process, the analyst provides samples, or training areas, that are representative of the different land cover types in the image. These sample points guide the classification algorithm to assign each pixel to a specific class. This method requires prior knowledge of the area being studied, and the accuracy of the classification depends on the quality and representativeness of the training data.

The process of supervised classification can be broken down into several key steps:

### 1. Selection of Training Data
   The first step in supervised classification is to select training data. Training data consists of sample pixels or regions from the image that are known to belong to specific classes. These samples serve as references for the classification process. The analyst identifies areas that correspond to specific land cover types such as water, forest, urban, or agricultural areas. It is crucial that the training data is representative of ______ ____ ________ _________ ________ _____.
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