Multispectral Landsat image classification using a data clustering algorithm

• Published in: Proceedings of 2004 International Conference on Machine Learning and Cybernetics : August 6-29, 2004, Worldfield Convention Hotel, Shanghai, China Vol. 7; pp. 4380 - 4384 vol.7
• Main Authors: Yan Wang, Mo Jamshidi, Neville, P., Bales, C., Morain, S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2004
• Subjects: Clustering algorithms; Content addressable storage; Gaussian distribution; Humans; Image classification; Indexing; Neural networks; Remote sensing; Satellites; Self organizing feature maps
• ISBN: 0780384032; 9780780384033
• DOI: 10.1109/ICMLC.2004.1384607

This work presents a new application of a data-clustering algorithm in Landsat image classification, which improves on conventional classification methods. Neural networks have been widely used in Landsat image classification because they are unbiased by data distribution. However, they need long training times for the network to get satisfactory classification accuracy. The data-clustering algorithm is based on fuzzy inferences using radial basis functions and clustering in input space. It only passes training data once so it has a short training tune. It can also generate fuzzy classification, which is appropriate in the case of mixed, intermediate or complex cover pattern pixels. This algorithm is applied in the land cover classification of Landsat 7 ETM+ over the Rio Rancho area, New Mexico. It is compared with back-propagation neural network (BPNN) to illustrate its effectiveness and concluded that it can get a better classification using shorter training time.