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.
Highlights
Texture is one of the most important spatial features of an image
The obtained results showed a high efficiency of spectro-textural classification based on the results of granulometric analysis
CoTnhcleupsrieosnesnted studies showed the advantage of granulometric analysis over the other two methods of texTthuerapl raensaelnytseids (GstuLdCiMes asnhdowLaepdlatcheefialtdevrsa)nitnagtheeoefxgamrainnueldomaseptericct.aAnlallytessitsedovvearritahnetsoitnhcerreatsweod mtheethacocdusraocfyteoxftuclraaslsaifincaalytisoinsbiansetdheoenxlyamoninsepdecatsrpaelcdt.atAal.lAtelstoe,dalvmaorisatnatlsl itnecstreedasveadritahnetsac(wcuirthacoynoefecxlcaespsitfiiocnat)iofntihnergerlantiuolnomtoetthriecaapnparlyosaicshshboasweeddognrleyaotenr sepffiecatrcayl tdhaatna.aAllltshoe, variants based on the other two methods of textural analysis
Summary
Texture is one of the most important spatial features of an image. Compared to other important spatial features, such as shape and size, it is relatively simple to use because it does not require prior image segmentation. It is a distinctive feature of selected land use/cover classes, compared to other classes exhibiting significant spectral similarities. The use of textural information in classification, apart from spectral data, can significantly increase the accuracy of classification [1,2,3,4,5,6,7,8,9,10,11,12]. The best results can be obtained by using a combination of spectral and textural data [7,8,12]
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