Which of the following are the two main methods of image classification?

B is the correct choice because image classification methods can generally be categorized into two main approaches: supervised and unsupervised classification.

In supervised classification, the analyst provides labeled training data, which helps the algorithm learn to differentiate between various classes based on the features present in the images. This method requires prior knowledge of the classes being analyzed and is often more accurate when sufficient training data is available.

Unsupervised classification, on the other hand, does not require predetermined labels. Instead, it groups pixels based on their spectral characteristics alone, allowing the algorithm to identify patterns or clusters within the data without prior knowledge about the classes.

These two methods form the foundation of remote sensing image analysis and are widely employed for various applications, such as land cover mapping and habitat modeling.

The other options refer to specific techniques or processes related to classification rather than overarching categories. For instance, maximum likelihood and neural networks are specific algorithms that can be used within supervised classification. Ground truthing and feature extraction are more about validating and preparing data rather than classification methods themselves. Binary and multi-class classification describe the types of output that can result from the classification process but do not represent distinct methods of classification.