Tou, Julius T. and Rafael C. Gonzalez. 1974. Pattern Recognition Principles. Addison-Wesley Publishing Co.The ISODATA method is similar in principle to the K-means procedure in the sense that cluster centers are iteratively determined sampled means. Unlike the latter algorithm, however, ISODATA represents a fairly comprehensive set of additional heuristic procedures which have been incorporated into an interactive scheme. Up to 16 image channels and 255 clusters can be found in this program. The program reads in image data from a file specified by the FILE parameter. The input channels can be specified through the DBIC parameter, which can accept up to 16 different image channels.
Due to the large amount of memory required, ISOCLUS will sample a subset of the image data during cluster mean calculations to generate a histogram. The amount of sampling depends on the amount of image data.
For example, for a 1024x1024 image data, ISOCLUS will sample every other pixel during calculation of cluster means. However, when writing results to an output channel, all pixels will be classified.
The MASK parameter specifies the area within the input channel which will be processed. Only the area under mask will be classified and the rest of the image will not be processed. If a single value is specified, then this value refers to a bitmap segment, which defines the area to be classified. When four values are specified, these values define the x,y offsets and x,y dimensions of a rectangular window within the image to be classified. If defaulted, the entire database is processed.
It is quite common for satellite images to have a lot of black- filled areas (with zero gray levels) which should not be included in the classification. To solve this problem, the user can first run the program THR by setting the TVAL's minimum and maximum values to 1 and 255, respectively. A bitmap mask is thus created only on the image area. The user then inputs this bitmap as the MASK parameter in this program.
The user can specify the number of clusters desired through the NUMCLUS parameter. It should be noted that this specification only provides an estimate; the final number of clusters may vary. However, the user may limit the variation by setting the parameters MAXCLUS and MINCLUS. MAXCLUS limits the total number of clusters allowed during splitting, and MINCLUS limits the minimum number of clusters allowed during lumping.
The initial seed values can be entered in a text file and specified by the SEEDFILE parameter. If no filename is provided in the SEEDFILE parameter, seeds will be generated diagonally along the n-dimensional histogram.
The user should define the maximum number of iterations allowed for the program through the parameter MAXITER, and the movement threshold through the parameter MOVETHRS. The program terminates when the number of iterations reaches MAXITER, or when the movement of all cluster means is less than MOVETHRS. For example, for all cluster means, the following situation terminates the program:
New cluster mean position - Old cluster mean position
----------------------------------------------------- < MOVETHRS
Old cluster mean position
The result of the clustering is a theme map directed to a specified
database image channel. If this channel is not specified, the
results will not be saved into a channel. A theme map encodes each
cluster with a unique grey level. Cluster number is represented by
grey level. For example, cluster 1 is assigned with the grey level
of 1, and cluster 2 is assigned with the grey level of 2. Grey level
0 represents unclassified clusters. Therefore, if the theme map is
later directed to the display, a pseudocolour table should be loaded
so that each cluster is represented by a different colour.ISOCLUS includes the option of generating signatures for each cluster through the parameter SIGGEN. If SIGGEN is YES, a signature for each cluster will be generated. The user can use the MLC (Maximum Likelihood Classifier) program to classify other images.
ISOCLUS allows the user to specify a background grey level value (BACKVAL) to be ignored during classification. If this value is specified, pixels with background grey level value will be assigned class 0 (null class).
After the execution of this program, the user may run the `AGGREG' program to view clusters or aggregate clusters. See the `AGGREG' documentation for more details.