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

Przemysław Kupidura

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.
Author Przemysław Kupidura (FGC / DPTSIS)
Przemysław Kupidura,,
- Department of Photogrammetry, Teledetection and Spatial Information Systems
Journal seriesRemote Sensing, ISSN 2072-4292, (A 35 pkt)
Issue year2019
Vol11
No10
Pages1-20
Publication size in sheets0.95
Keywords in Englishsatellite imagery; classification; texture analysis; GLCM; mathematical morphology; granulometric analysis; Laplace filter
ASJC Classification1507 Fluid Flow and Transfer Processes; 1706 Computer Science Applications; 1508 Process Chemistry and Technology; 2200 General Engineering; 3105 Instrumentation; 2500 General Materials Science
DOIDOI:10.3390/rs11101233
Internal identifier10/2019
Languageen angielski
File
10_2019 Kupidura The Comparison.pdf 6.73 MB
Additional file
10_2019 Oświadczenie Kupidura The Comparison.pdf 417.66 KB
Score (nominal)35
ScoreMinisterial score = 35.0, 27-05-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.559; WoS Impact Factor: 2017 = 3.406 (2) - 2017=3.952 (5)
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