Next Article in Journal
Influence of Stress and Crack Patterns on the Sensitive Characteristics of Fissure Sandstone Permeability under Hydromechanical Coupling
Next Article in Special Issue
Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance
Previous Article in Journal
Dual Zero-Watermarking Scheme for Two-Dimensional Vector Map Based on Delaunay Triangle Mesh and Singular Value Decomposition
Previous Article in Special Issue
Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks
Article Menu
Issue 4 (February-2) cover image

Export Article

Open AccessArticle
Appl. Sci. 2019, 9(4), 643;

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Department of Geoinformatic Engineering, Inha University, Incheon 22212, Korea
Author to whom correspondence should be addressed.
Received: 28 January 2019 / Revised: 12 February 2019 / Accepted: 12 February 2019 / Published: 14 February 2019
Full-Text   |   PDF [4313 KB, uploaded 19 February 2019]   |  


Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often used for crop classification. From a data availability viewpoint, it is not always possible to acquire time-series UAV images due to limited accessibility to the study area. Thus, it is necessary to improve classification performance for situations when a single or minimum number of UAV images are available for crop classification. In this study, we investigate the potential of gray-level co-occurrence matrix (GLCM)-based texture information for crop classification with time-series UAV images and machine learning classifiers including random forest and support vector machine. In particular, the impact of combining texture and spectral information on the classification performance is evaluated for cases that use only one UAV image or multi-temporal images as input. A case study of crop classification in Anbandegi of Korea was conducted for the above comparisons. The best classification accuracy was achieved when multi-temporal UAV images which can fully account for the growth cycles of crops were combined with GLCM-based texture features. However, the impact of the utilization of texture information was not significant. In contrast, when one August UAV image was used for crop classification, the utilization of texture information significantly affected the classification performance. Classification using texture features extracted from GLCM with larger kernel size significantly improved classification accuracy, an improvement of 7.72%p in overall accuracy for the support vector machine classifier, compared with classification based solely on spectral information. These results indicate the usefulness of texture information for classification of ultra-high-spatial-resolution UAV images, particularly when acquisition of time-series UAV images is difficult and only one UAV image is used for crop classification. View Full-Text
Keywords: unmanned aerial vehicle; texture; gray-level co-occurrence matrix; machine learning; crop unmanned aerial vehicle; texture; gray-level co-occurrence matrix; machine learning; crop

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Kwak, G.-H.; Park, N.-W. Impact of Texture Information on Crop Classification with Machine Learning and UAV Images. Appl. Sci. 2019, 9, 643.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top