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Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data

Performance evaluation of textural features in improving land use/land cover classification... Texture analysis of remote sensing images has been received a substantial amount of attention as it plays a vital role in improving the classification accuracy of heterogeneous landscape. However, it is inadequately studied that how the images from different sensors with varying spatial resolutions influence the choice of textural features. This study endeavors to examine the textural features from the Landsat 8-OLI, RISAT-1, Resourcesat 2-LISS III, Sentinel-1A and Resourcesat 2-LISS IV satellite images with spatial resolution of 30, 25, 23.5, 5×20 and 5.8 m respectively, for improving land use/land cover (LULC) classification accuracy. The textural features were extracted from the aforesaid sensor data with the assistance of gray-level co-occurrence matrix (GLCM) with different moving window sizes. The best combination of textural features was recognized using standard deviations and correlation coefficients following separability analysis of LULC categories based on training samples. A supervised support vector machine (SVM) classifier was employed to perform LULC classification and the results were evaluated using ground truth information. This work demonstrates the significance of textural features in improving the classification accuracy of heterogeneous landscape and it becomes more significant as the spatial resolution improved. It is also revealed that textures are vital especially in the case of SAR data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Earth Science Informatics Springer Journals

Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data

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References (54)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Earth Sciences; Earth Sciences, general; Information Systems Applications (incl.Internet); Simulation and Modeling ; Ontology; Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics) ; Earth System Sciences
ISSN
1865-0473
eISSN
1865-0481
DOI
10.1007/s12145-018-0369-z
Publisher site
See Article on Publisher Site

Abstract

Texture analysis of remote sensing images has been received a substantial amount of attention as it plays a vital role in improving the classification accuracy of heterogeneous landscape. However, it is inadequately studied that how the images from different sensors with varying spatial resolutions influence the choice of textural features. This study endeavors to examine the textural features from the Landsat 8-OLI, RISAT-1, Resourcesat 2-LISS III, Sentinel-1A and Resourcesat 2-LISS IV satellite images with spatial resolution of 30, 25, 23.5, 5×20 and 5.8 m respectively, for improving land use/land cover (LULC) classification accuracy. The textural features were extracted from the aforesaid sensor data with the assistance of gray-level co-occurrence matrix (GLCM) with different moving window sizes. The best combination of textural features was recognized using standard deviations and correlation coefficients following separability analysis of LULC categories based on training samples. A supervised support vector machine (SVM) classifier was employed to perform LULC classification and the results were evaluated using ground truth information. This work demonstrates the significance of textural features in improving the classification accuracy of heterogeneous landscape and it becomes more significant as the spatial resolution improved. It is also revealed that textures are vital especially in the case of SAR data.

Journal

Earth Science InformaticsSpringer Journals

Published: Oct 28, 2018

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