Give an overview of image classification methods.

Image classification is a fundamental process in remote sensing and image interpretation, aiming to categorize pixels in an image into meaningful classes. Various classification methods are employed depending on the nature of the data, the desired level of accuracy, and the available computational resources. These methods can be broadly divided into supervised, unsupervised, and object-based classification techniques.  

Supervised Classification  
Supervised classification is a technique where the user provides training samples for known land cover types. The classifier then assigns pixels to the most appropriate class based on statistical parameters derived from these training areas. Common algorithms under supervised classification include:  

- Maximum Likelihood Classifier (MLC): This method assumes that the spectral values of each class follow a normal distribution and assigns pixels to the class with the highest probability. _________ _____ ____ ________ ___ ______ _____ ______ _____ ________.
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