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ISPRS Int. J. Geo-Inf. 2014, 3(2), 540-553; doi:10.3390/ijgi3020540

Land Cover Heterogeneity Effects on Sub-Pixel and Per-Pixel Classifications

1
Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK 73019, USA
2
Department of Geography, Texas State University, San Marcos, TX 78666, USA
*
Author to whom correspondence should be addressed.
Received: 1 January 2014 / Revised: 3 March 2014 / Accepted: 17 March 2014 / Published: 3 April 2014
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Abstract

Per-pixel and sub-pixel are two common classification methods in land cover studies. The characteristics of a landscape, particularly the land cover itself, can affect the accuracies of both methods. The objectives of this study were to: (1) compare the performance of sub-pixel vs. per-pixel classification methods for a broad heterogeneous region; and (2) analyze the impact of land cover heterogeneity (i.e., the number of land cover classes per pixel) on both classification methods. The results demonstrated that the accuracy of both per-pixel and sub-pixel classification methods were generally reduced by increasing land cover heterogeneity. Urban areas, for example, were found to have the lowest accuracy for the per-pixel method, because they had the highest heterogeneity. Conversely, rural areas dominated by cropland and grassland had low heterogeneity and high accuracy. When a sub-pixel method was used, the producer’s accuracy for artificial surfaces was increased by more than 20%. For all other land cover classes, sub-pixel and per-pixel classification methods performed similarly. Thus, the sub-pixel classification was only advantageous for heterogeneous urban landscapes. Both creators and users of land cover datasets should be aware of the inherent landscape heterogeneity and its potential effect on map accuracy. View Full-Text
Keywords: Landsat; classification; land cover heterogeneity; remote sensing Landsat; classification; land cover heterogeneity; remote sensing
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Tran, T.V.; Julian, J.P.; de Beurs, K.M. Land Cover Heterogeneity Effects on Sub-Pixel and Per-Pixel Classifications. ISPRS Int. J. Geo-Inf. 2014, 3, 540-553.

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