Image Classification and Analysis
A human analyst attempting to
classify features in an image uses the elements of visual interpretation to identify homogeneous groups of pixels which
represent various features or land cover classes of interest. Digital
image classification uses the spectral information represented by the
digital numbers 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. In either
case, the objective is to assign all pixels in the image to particular classes
or themes (e.g. water, coniferous forest, deciduous forest, corn, wheat, etc.).
The resulting classified image is comprised of a mosaic of pixels, each of
which belong to a particular theme, and is essentially a thematic
"map" of the original image.
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 channels 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 sub-classes with
unique spectral variations. Using the forest example, spectral sub-classes may
be due to variations in age, species, and density, or perhaps as a result of
shadowing or variations in scene illumination. It is the analyst's job to
decide on the utility of the different spectral classes and their
correspondence to useful information classes.
Common classification procedures
can be broken down into two broad subdivisions based on the method used: supervised
classification and unsupervised classification. In a 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.
The selection of appropriate training areas is based on the analyst's
familiarity with the geographical area and their knowledge of the actual
surface cover types present in the image. 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 are 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 labeled as the class it most closely
"resembles" digitally. Thus, in a supervised classification we are
first identifying the information classes which are then used to determine the
spectral classes which represent them.
Unsupervised classification in essence 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. The final result of this iterative clustering process may result in
some clusters that the analyst will want to subsequently combine, or clusters
that should be broken down further - each of these requiring a further
application of the clustering algorithm. Thus, unsupervised classification is
not completely without human intervention. However, it does not start with a
pre-determined set of classes as in a supervised classification.
0 Comments