What is supervised classification in remote sensing?

Supervised classification in remote sensing involves using labeled data to train algorithms to categorize pixels in satellite or aerial imagery. The labeled data consists of samples from various land cover classes (such as water, forest, urban areas, etc.) that have been previously identified and annotated. This training allows the algorithm to learn the relationship between the characteristics of the data (like spectral signatures, texture, and patterns) and the corresponding land cover types.

Once the algorithm has been trained, it can then be applied to unlabeled imagery to predict the class of each pixel based on the learned patterns from the training data. This method is particularly effective because it leverages existing knowledge to improve classification accuracy, thus providing more reliable results for environmental monitoring, land-use planning, and various other applications in remote sensing.