Unlike other aerial photographic and satellite image interpretation work, these multispectral images do not make it easy to identify directly the feature type by visual inspection. Hence the remote sensing data has to be classified first, followed by processing by various data enhancement techniques so as to help the user to understand the features that are present in the image.
Such classification is a complex task which involves rigorous validation of the training samples depending on the classification algorithm used. The techniques can be grouped mainly into two types.
* Supervised classification techniques
* Unsupervised classification techniques
Supervised classification makes use of training samples. Training samples are areas on the ground for which there is ground truth, that is, what is there is known. The spectral signatures of the training areas are used to search for similar signatures in the remaining pixels of the image, and we will classify accordingly.  This use of training samples for classification is called supervised classification.  Expert knowledge is very important in this method since the selection of the training samples and a biased selection can badly affect the accuracy of classification.  Popular techniques include the maximum likelihood principle and convolutional neural network.  The Maximum likelihood principle calculates the probability of a pixel belonging to a class (i.e. feature) and allots the pixel to its most probable class. Newer convolutional neural network based methods  account for both spatial proximity and entire spectra to determine the most likely class.
In case of unsupervised classification no prior knowledge is required for classifying the features of the image. The natural clustering or grouping of the pixel values, i.e. the gray levels of the pixels, are observed. Then a threshold is defined for adopting the number of classes in the image. The finer the threshold value, the more classes there will be. However, beyond a certain limit the same class will be represented in different classes in the sense that variation in the class is represented. After forming the clusters, ground truth validation is done to identify the class the image pixel belongs to. Thus in this unsupervised classification apriori information about the classes is not required.  One of the popular methods in unsupervised classification is k-means clustering.