Segmentation of satellite imagery of natural scenes using data mining

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Issue Date
1999-03Author
Soh, Leen-Kiat
Tsatsoulis, Costas
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Type
Article
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In this paper, we describe a segmentation technique that integrates traditional image processing algorithms with techniques adapted from knowledge discovery in databases (KDD) and data mining to analyze and segment unstructured satellite images of natural scenes, We have divided our segmentation task into three major steps, First, an initial segmentation is achieved using dynamic local thresholding, producing a set of regions. Then, spectral, spatial, and textural features for each region are generated from the thresholded image. Finally, gi,en these features as attributes, an unsupervised machine learning methodology called conceptual clustering is used to cluster the regions found in the image into N classes-thus, determining the number of classes in the image automatically. We have applied the technique successfully to ERS-1 synthetic aperture radar (SAR), Landsat thematic mapper (TM), and NOAA advanced very high resolution radiometer (AVHRR) data of natural scenes.
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©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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Citation
Soh, LK; Tsatsoulis, C. Segmentation of satellite imagery of natural scenes using data mining. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. March 1999. 37(2, 2) 1086-1099
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