The classification includes a broad range of decision-theoretic approaches to identifying images (or parts thereof). All classification algorithms are based on the assumption that the image in question depicts one or more features (e.g., geometric details in the case of a manufacturing classification system, or spectral regions in the point of remote sensing, as shown in the examples below) and that each of these features belongs to one of several distinct and exclusive classes. The classes may be specified a priori by an analyst (as in supervised classification) or automatically clustered (i.e. as in unsupervised classification) into sets of prototype classes, where the analyst merely specifies the number of desired categories. (Classification and segmentation have closely related objectives, as the former is another form of component labeling that can result in the segmentation of various features in a scene.)
Image classification is the process of assigning land cover classes to pixels. It refers to the task of extracting information classes from a multiband raster image. The resulting raster from image classification can be used to create thematic maps. Depending on the interaction between the analyst and the computer during classification, there are two types of classification: supervised and unsupervised. Image classification plays an important role in environmental and socioeconomic applications. In order to improve classification accuracy, scientists have laid paths in developing advanced classification techniques.
Figure 1 Image classification
Image classification is perhaps the most important part of digital image analysis. It is very nice to have a "pretty picture" or an image, showing a magnitude of colors illustrating various features of the underlying terrain, but it is quite useless unless to know what the colors mean.
How does It work?
Image classification analyzes the numerical properties of various image features and organizes data into categories. Classification algorithms typically employ two phases of processing: training and testing. In the initial training phase, characteristic properties of typical image features are isolated, and, based on these, a unique description of each classification category, i.e. training class, is created. In the subsequent testing phase, these feature-space partitions are used to classify image features. The description of training classes is an extremely important component of the classification process. In supervised classification, statistical processes (i.e. based on an a priori knowledge of probability distribution functions) or distribution-free processes can be used to extract class descriptors. The unsupervised classification relies on clustering algorithms to automatically segment the training data into prototype classes. In either case, the motivating criteria for constructing training classes are that they are:
- Independent, e.a change in the description of one training class should not change the value of another,
- Discriminatory, e.different image features should have significantly different descriptions, and
- Reliable, all image features within a training group should share the common definitive descriptions of that group.
A convenient way of building a parametric description of this sort is via a feature vector, where n is the number of attributes, which describe each image feature and training class. This representation allows us to consider each image feature as occupying a point, and each training class as occupying a sub-space (i.e. a representative point surrounded by some spread, or deviation), within the n-dimensional classification space. Viewed as such, the classification problem is that of determining to which sub-space class each feature vector belongs.
Image Classification Technique
Two major categories of image classification techniques include unsupervised (calculated by the software) and supervised (human-guided) classification:
- Unsupervised Classification
Unsupervised classification is where the outcomes (groupings of pixels with common characteristics) are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as wetlands, developed areas, coniferous forests, etc.)
- Supervised Classification
Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Training sites (also known as testing sets or input classes) are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the training area, plus or minus a certain increment (often based on "brightness" or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into. Many analysts use a combination of supervised and unsupervised classification processes to develop final output analysis and classified maps.
 “Image Classification”, Tutorial, available online at: http://www.microimages.com/documentation/Tutorials/classify.pdf
 What is image classification? Available online at: http://desktop.arcgis.com/en/arcmap/latest/extensions/spatial-analyst/image-classification/what-is-image-classification-.htm
 S. V. S. Prasad , T. Satya Savithri and Iyyanki V. Murali Krishna, “Techniques in Image Classification; A Survey”, Global Journal of Researches in Engineering: F Electrical and Electronics Engineering, Volume 15, Issue 6, Version 1.0, Year 2015.Read More