Abstract
This paper presents a study on the effectiveness of texture analysis of remote sensing imagery depending on the type and spatial resolution of the source image. The study used the following image types: near-infrared band, red band, first principal component, second principal component and normalized difference vegetation index images of pixel size from 2 m to 30 m, generated from a multispectral WorldView-2 image. The study evaluated the separability of the selected pairs of the following land cover classes: bare soil, low vegetation, coniferous forest, deciduous forest, water reservoirs, built-up areas. The tool used for texture analysis was granulometric analysis based on morphological operations—one of less popular methods which, however, as demonstrated by previous studies, shows high effectiveness in separating classes of different texture. The conducted study enabled researchers to evaluate the significance of image type and resolution for visibility of texture in the image and the possibility of using texture to differentiate between classes. The obtained results showed that there is no single, universal combination of conditions of texture analysis, which would be the best from the point of view of all classes. For most of the analyzed pairs of classes, the best results were obtained for the highest spatial resolution of the image (2–3 m), but the class of built-up areas stands out in this comparison—the best distinction was obtained with the average spatial resolution (10–15 m). Research has also shown that there is no single type of image that is universally the best basis for texture analysis. While for the majority of classes the image of the first principal component was the best, for the class of built-up areas it was the image of the red channel.
Subject
General Earth and Planetary Sciences
Reference53 articles.
1. Texture Features in Remote Sensing Imagery;Simon;Digital Image Processing,1981
2. Pattern recognition from satellites altitudes;Darling;IEEE Trans. Syst. Man Cybern.,1968
3. Textural Features for Image Classification;Haralick;IEEE Trans. Syst. Man Cybern.,1973
4. A Comparative Study of Texture measures for Terrain Classification;Weszka;IEEE Trans. Syst. Man Cybern.,1976
5. Classification of multispectral image data by extraction and classification of homogeneous objects;Kettig;IEEE Trans. Geosci. Electron.,1976
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