D-ISODATA: A Distributed Algorithm for Unsupervised Classification of Remotely Sensed Data on Network of Workstations

With the advent of high-speed networks and the availability of powerful high-performance workstations, network of workstations has emerged as the most cost-effective platform for computation-intensive applications. One of the major applications for the network of workstations is in the field of remote sensing, where because of the high dimensionality of data, most of the existing data exploitation procedures are computation-intensive. To test the utility of the network of workstations in the field of remote sensing we have adopted a modified version of the well-known ISODATA classification procedure which may be considered as the benchmark for all unsupervised classification algorithms. The ISODATA algorithm is an iterative method that uses Euclidean distance as the similarity measure to cluster data elements into different classes. We have designed and developed a distributed version of ISODATA algorithm (D-ISODATA) on the network of workstations under a message-passing interface environment and have obtained promising speedup. To reduce the processing load and thereby increase the throughput, the ISODATA procedure is commonly applied to only the first few principal component images derived from the original set of the multispectral images. The drawback with the principal component approach is that it is based entirely on the statistical significance of the spectra, rather than the uniqueness of the individual spectra. As, small objects and ground features would likely manifest themselves in the last principal component images, that is, eigen images, discarding them prior to classification would lead to the loss of valuable information. The significant enhancement in processing speed on the network of workstations makes it possible for us to apply our distributed algorithm D-ISODATA to the entire set of multispectral images directly, thereby preserving all the spectral signatures in the data, regardless of their statistical significance.