The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery

Abstract

The paper presents a comparison of the efficacy of several texture analysis methods as tools for improving land use/cover classification in satellite imagery. The tested methods were: gray level co-occurrence matrix (GLCM) features, Laplace filters and granulometric analysis, based on mathematical morphology. The performed tests included an assessment of the classification accuracy performed based on spectro-textural datasets: spectral images with the addition of images generated using different texture analysis methods. The class nomenclature was based on spectral and textural differences and included the following classes: water, low vegetation, bare soil, urban, and two (coniferous and deciduous) forest classes. The classification accuracy was assessed using the overall accuracy and kappa index of agreement, based on the reference data generated using visual interpretation of the images. The analysis was performed using very high-resolution imagery (Pleiades, WorldView-2) and high-resolution imagery (Sentinel-2). The results show the efficacy of selected GLCM features and granulometric analysis as tools for providing textural data, which could be used in the process of land use/cover classification. It is also clear that texture analysis is generally a more important and effective component of classification for images of higher resolution. In addition, for classification using GLCM results, the Random Forest variable importance analysis was performed.

The potential of UAV-borne spectral and textural information for predicting aboveground biomass and N fixation in legume-grass mixtures

“…Furthermore, Culbert et al [38] documented that texture features can vary depending on the observed vegetation and its phenological stage, which makes it an interesting tool for multi-temporal studies. These facts are gaining more attention in research studies, mainly focusing on land cover classification [39][40][41], vegetation modelling [42,43] and structure [44,45] as well as forest biomass estimation [46,47]. For agricultural crops, there exist, to our best knowledge, only two studies using texture features in combination with UAV multispectral information.…”

Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

“…There are several indices derived from second-order statistical of GLCM reflecting texture characteristics. In our study, we adopted three texture indices called Contrast (CON), Entropy (ENT), and Correlation (COR), which have been proven to be effective in identifying heterogeneous objects [43][44][45]. These three texture indices are determined by following equations:…”

Monitoring of post-fire forest scars in Serbia based on satellite Sentinel-2 data

“…The use of textural information in image classification, apart from spectral data, can significantly increase the accuracy of classification (Haralick et al 1973). The best results can be obtained by using a combination of spectral and textural data (Bekkari et al 2012;Kupidura 2019). Texture can be a distinctive feature of selected land cover classes exhibiting significant spectral similarities.…”

“…It has no unambiguous definition (Bracewell 2000), which is why in the practice of digital image processing many different methods of texture analysis have been defined. For this study, Fourier transform (Kupidura 2019) and computation of image statistics were applied for texture characterizations of study sites.…”