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Land-Use and Land-Cover Mapping Using a Gradable Classification Method
AbstractConventional spectral-based classification methods have significant limitations in the digital classification of urban land-use and land-cover classes from high-resolution remotely sensed data because of the lack of consideration given to the spatial properties of images. To recognize the complex distribution of urban features in high-resolution image data, texture information consisting of a group of pixels should be considered. Lacunarity is an index used to characterize different texture appearances. It is often reported that the land-use and land-cover in urban areas can be effectively classified using the lacunarity index with high-resolution images. However, the applicability of the maximum-likelihood approach for hybrid analysis has not been reported. A more effective approach that employs the original spectral data and lacunarity index can be expected to improve the accuracy of the classification. A new classification procedure referred to as “gradable classification method” is proposed in this study. This method improves the classification accuracy in incremental steps. The proposed classification approach integrates several classification maps created from original images and lacunarity maps, which consist of lacnarity values, to create a new classification map. The results of this study confirm the suitability of the gradable classification approach, which produced a higher overall accuracy (68%) and kappa coefficient (0.64) than those (65% and 0.60, respectively) obtained with the maximum-likelihood approach.
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Kitada, K.; Fukuyama, K. Land-Use and Land-Cover Mapping Using a Gradable Classification Method. Remote Sens. 2012, 4, 1544-1558.View more citation formats
Kitada K, Fukuyama K. Land-Use and Land-Cover Mapping Using a Gradable Classification Method. Remote Sensing. 2012; 4(6):1544-1558.Chicago/Turabian Style
Kitada, Keigo; Fukuyama, Kaoru. 2012. "Land-Use and Land-Cover Mapping Using a Gradable Classification Method." Remote Sens. 4, no. 6: 1544-1558.
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