Classification is a process during which different classes of an image are separated from each other. Usually, the classification result is displayed using different colors.
There are two main types of classification.
- Supervised Classification
- Unsupervised Classification
The difference between these two methods is in the type of input data and the classification algorithm. When we use supervised classification, we have to give a series of labels along with input images to the program. Using the given labels, the algorithm learns the characteristics of our class and by generalizing it, it can use the trained classification in the whole set of the input image.
But the unsupervised classification does not need any labels. For several reasons, we may not be able to find suitable labels to train the algorithm. Unsupervised classification helps us in these kinds of situations. One of the main methods used in unsupervised classification by the algorithm is creating fake labels.
In this project, we will examine different classification methods in Envi software.
The following steps have been done in this project:
- Atmospheric correction of the image
- calculation of reflectance
- Unsupervised Classification by ISODATA method
- Unsupervised Classification by K-Means method
- Supervised Classification by Mahalanobis method
- Supervised Classification by Maximum likelihood method
- DEM extraction of the area to use in the decision-tree classification method
- Decision-tree classification method
The complete version of the article is available through the following link: