Procedures of Image Classification: Algorithm for Supervised and Unsupervised Classification

Image Classification has formed an important part of the fields of Remote Sensing, Image Analysis and Pattern Recognition. In some instances, the classification itself may form the object of the analysis.

Assumptions of Digital Image Classification

Digital Image Classification is the process of sorting all the pixels in an image into a finite number of individual classes. The classification process is based on the following assumptions:

Patterns of their DN, usually in multichannel data (Spectral Classification).

Spatial relationship with neighbouring pixels

Relationships between the data acquired on different dates.

Forms and Objectives of Digital Image Classification

Pattern Recognition, Spectral Classification, Textural Analysis and Change Detection are different forms of classification that are focused on 3 main objectives:

Detection of different kinds of features in an image.

Discrimination of distinctive shapes and spatial patterns

Identification of temporal changes in image

Fundamentally spectral classification forms the bases to map objectively the areas of the image that have similar spectral reflectance/emissivity characteristics. Depending on the type of information required, spectral classes may be associated with identified features in the image (supervised classification) or may be chosen statistically (unsupervised classification).

The classification has also seen to compressing image data by reducing the large range of DN in several spectral bands to a few classes in a single image. Classification reduces this large spectral space into relatively few regions and obviously results in loss of numerical information from the original image. There is no theoretical limit to the dimensionality used for the classification, though obviously the more bands involved, the more computationally intensive the process becomes. It is often wise to remove redundant bands before classification. The informational data are classified into systems:

Supervised

Unsupervised

Algorithm of Supervised Classification

With supervised classification, we identify examples of the Information classes (i.e.. , land cover type) of interest in the image. These are called “training sites”. The image processing software system is then used to develop a statistical characterization of the reflectance for each information class. It is done in following stages:

This stage is often called “signature analysis” and may involve developing a characterization as simple as the mean or the rage of reflectance on each band, or as complex as detailed analyses of the mean, variances and covariance over all bands. Once a statistical characterization has been achieved for each information class, the image is then classified by examining the reflectance for each pixel and making a decision about which of the signatures it resembles most. (Eastman, 1995).

In supervised classification, the analyst identifies in the imagery homogeneous representative samples of the different surface cover types (information classes) of interest. These samples are referred to as training areas. Thus, the analyst is “supervising” the categorization of a set of specific classes.

The numerical information in all spectral bands for the pixels comprising these areas is used to “train” the computer to recognize spectrally similar areas for each class. The computer uses a special program or algorithm (of which there are several variations), to determine the numerical “signatures” for each training class.

Once the computer has determined the signatures for each class, each pixel in the image is compared to these signatures and labelled as the class it most closely “resembles” digitally.

Unsupervised classification

Unsupervised classification reverses the supervised classification process. Spectral classes are grouped first, based solely on the numerical information in the data, and are then matched by the analyst to information classes (if possible).

Programs, called clustering algorithms, are used to determine the natural (statistical) groupings or structures in the data. Usually, the analyst specifies how many groups or clusters are to be looked for in the data.

In addition to specifying the desired number of classes, the analyst may also specify parameters related to the separation distance among the clusters and the variation within each cluster.