In this study, we investigate an unsupervised learning algorithm for the segmentation of remote sensing images in which the optimum number of clusters is automatically estimated, and the clustering quality is checked. The computational load is also reduced as compared to a single stage algorithm. The algorithm has two stages. At the first stage of the algorithm, the self-organizing map was used to obtain a large number of prototype clusters. At the second stage, these prototype clusters were further clustered with the K-means clustering algorithm to obtain the final clusters. A clustering validity checking method, Davies-Bouldin validity checking index, was used in the second stage of the algorithm to estimate the optimal number of clusters in the data set.

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Absrroct-i n this study, targets and nontargets in a multispectral image were characterized in terms of their spectral features. Then, target detection procedures were performed. Target detection problem was considered as a nvo-class classification problem with four-band (Red-Green-Blue-Near infrared) images.For this purpose, statistical techniques were employed. These are Parallelepiped, Euclidean Distance and Maximum Likelihood (ML) algorithms, which beIong to supervised statistical classification methods. To obtain the training data belonging to each class, the training regioas were selected as polygonal. After determination of the parameters of the algorithms with the training set, classification was accomplished at each pixel as target or background. Consequently, ciassification results were displayed on thematic maps.The algorithms were trained with the same training sets, and their comparative performances were tested under various situations. During these studies, the effects of training area selection and various levels of thresholds wsere evaluated based on the efficiency of the algorithms. The selection of appropriate

A multistage parallel algorithm with iterative processing is discussed for the processing of information in diffraction tomography. The algorithm is based on matrix partitioning, which results in mostly parallel stages of processing. Each successive stage is designed to minimize the remaining error. The process is iterated until convergence. The major advantages of the multistage algorithm are the reduced computational time from faster convergence as compared with a single-stage iterative algorithm, further reduction of computation time if the stages are implemented mostly in parallel, and better performance in terms of reduced reconstruction error.