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TitleA Comparison of Clustering Strategies for Unsupervised Classification
DownloadDownloads (Preprint)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorCihlar, J; Latifovic, R; Beaubien, J
SourceCanadian Journal of Remote Sensing vol. 26, issue 5, 2000 p. 446-454,
Alt SeriesEarth Sciences Sector, Contribution Series 20042805
PublisherInforma UK Limited
Mediapaper; on-line; digital
File formatpdf
Subjectsremote sensing; mapping techniques; LANDSAT; Classification by Progressive Generalization (CPG); fuzzy K-means (FKM)
Illustrationstables; graphs; histograms
Released2014 07 31
AbstractLand cover mapping from multispectral satellite data is based primarily on spectral differences in land cover categories. Since only a limited number of cover types are desired in most cases, the images contain redundant information which unnecessarily complicates the digital mapping process. In this study, we have devised an algorithm to automatically and reproducibly quantize an image to be classified into a reduced number of digital levels, in most cases without a visually perceptible reduction in the image information content. The Flexible Histogram Quantization (FHQ) algorithm assumes that the histogram has one or two major peaks (representing water and/or land) and that most of the information of interest is in one peak. It aims to provide a sufficient quantization in the main peak of interest as well as in the tails of this peak by computing an optimized number of quantized levels and then identifying the range of digital values belonging to each level. A comparison of the FHQ with four existing quantization algorithms showed that the FHQ retained substantially more radiometric discrimination than histogram normalization, linear quantization, and scaling methods. Using a random sample of Landsat TM images and an AVHRR coverage of Canada, the average quantization error for the FHQ was 1.68 digital levels for an entire scene and 1.41 for land pixels only. Based on the 34 single-band test images included in the comparison, the radiometric resolution was reduced from 255 to 23.3 levels on the average, or by a factor of 10.94n for a multispectral image with n spectral bands. Compared to the other quantization methods, FHQ had a higher efficiency (by 65% to 148%), except for histogram equalization. FHQ also retained more information than histogram equalization (by 11%) but more importantly, it provided finer resolution in the tails of the main histogram peak (by 36 - 664%, depending on the position in the tails) for infrequent but potentially important land cover types. In addition, unlike the other methods the FHQ does not require a user-specified number of levels and therefore its results are fully reproducible. The FHQ can be used with single scenes, with radiometrically seamless mosaics, or when classifying radiometrically incompatible adjacent scenes. It is concluded that the FHQ provides an effective means for image quantization, as an automated pre-processing step in land cover mapping applications.

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