Supervised classification in remote sensing is a method of using labeled training data to automatically classify pixels or areas in an image. The process typically involves the following steps:
Collecting and preprocessing the image data: This includes acquiring the image data from a remote sensing platform such as a satellite or aircraft, and performing any necessary preprocessing steps such as atmospheric correction or geometric rectification.
Defining training areas and collecting training data: This involves manually identifying and labeling different classes of land cover within the image, such as forests, urban areas, or water bodies. These labeled training areas are used to train the classification algorithm.
Training the classification algorithm: A supervised classification algorithm such as maximum likelihood, decision tree, or support vector machine is used to learn the relationship between the image data and the labeled training data.
Applying the trained classification algorithm to the image: The trained algorithm is then applied to the entire image to classify each pixel or area into one of the defined classes.
Post-classification processing and accuracy assessment: The final classified image is then visually inspected and evaluated for accuracy using metrics such as overall accuracy, kappa coefficient, and confusion matrix.
For illustration, imagine a satellite image of a city. The image is preprocessed to correct for atmospheric effects and geometric distortions. The analyst then defines several training areas within the image, such as a residential area, a commercial area, and a park. They label each of these areas with their corresponding land-use class, such as residential, commercial, and park. The classification algorithm is then trained using these labeled training areas. Once the algorithm is trained, it is applied to the entire image, resulting in a classified image where each pixel is assigned a class label, such as residential, commercial, or park.
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