Ask AI: In supervised image classification, why must training sites be digitized and how are they related to the term “supervised”

In supervised image classification, training sites must be digitized for several reasons, and their relationship with the term "supervised" is significant. 1. Ground-truth data: Supervised image classification relies on ground-truth data, which means we need to have prior knowledge of the land cover types in a region of interest. Digitizing training sites is the process of selecting representative regions for each land cover type, which serves as the reference data. 2. Training the classifier: The supervised classification algorithm needs a set of examples (training data) to learn the relationship between the image pixel values and the corresponding land cover types. These training sites, selected by the user, must be digitized to create a representative training dataset. 3. Defining spectral signatures: By digitizing training sites, we can extract the spectral signatures – the unique combination of spectral values for each land cover type. These signatures are crucial as they are used by the supervised classification algorithm to differentiate between different land cover types. 4. Improving accuracy: By having well-defined and representative training sites, the supervised classification algorithm can be more accurate in its predictions. A higher number of representative digitized sites leads to better training of the Classifier, which translates to more accurate land cover classification results. The term "supervised" refers to the active participation of the user or expert during the classification process. The user provides guidance by digitizing the training sites and assigning them to specific land cover types. This information is the foundation for the supervised classification algorithm to differentiate between various land cover types and generate an accurate classified image.