Introduction
The objective of digital image classification is to assign all pixels in the image to particular classes or themes (e.g., water, coniferous forest, deciduous forest, corn, wheat). The resulting classified image comprises a mosaic of pixels, each of which belong to a particular theme, and is essentially a thematic “map” of the original image. There is a variety of approaches to perform digital image classification. Spectral image classification uses the spectral information represented by digital numbers (pixel values) in one or more spectral bands, and attempts to classify each individual pixel based on this spectral information. This type of classification is termed spectral pattern recognition.
When talking about classes, we need to distinguish between information classes and spectral classes. Information classes are those categories of interest that the analyst is actually trying to identify in the imagery, such as different kinds of crops, different forest types or tree species, different geologic units or rock types, etc. Spectral classes are groups of pixels that are uniform (or near-similar) with respect to their brightness values in the different spectral bands of the data. The objective is to match the spectral classes in the data to the information classes of interest. Rarely is there a simple one-to-one match between these two types of classes. Rather, unique spectral classes may appear which do not necessarily correspond to any information class of particular use or interest to the analyst. Alternatively, a broad information class (e.g., forest) may contain a number of spectral subclasses with unique spectral variations. Using the forest example, spectral subclasses may be due to variations in age, species, and density, or perhaps as a result of shadowing or variations in scene illumination. Also keep in mind that the appearance of classes through time might not be homogenous (e.g., seasonal effects). It is the analyst’s job to decide on the utility of the different spectral classes and their correspondence to useful information classes.
We begin first by covering the concept of image space versus feature space and how this is used in classifying images.
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Classification Types
There are many classification algorithms available, in this clip you will be introduced to some of them as well as to things you should consider when conducting a classification on your remote sensing images.
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Training Data Collection
Another important factor to consider in classification is the training data collection, including how this data is gathered.
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