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Open AccessArticle

Object-Based Image Classification of Summer Crops with Machine Learning Methods

1
Institute for Sustainable Agriculture, IAS-CSIC, P.O. Box 4084, E-14080 Córdoba, Spain
2
Department of Plant Sciences, University of California, Davis, CA 95616, USA
3
Department of Computer Science and Numerical Analysis, University of Cordoba, Campus de Rabanales, E-14071 Córdoba, Spain
4
Department of Environmental Systems Sciences, Swiss Federal Institute of Technology, ETH-Zurich, CH-8092 Zurich, Switzerland
*
Author to whom correspondence should be addressed.
Remote Sens. 2014, 6(6), 5019-5041; https://doi.org/10.3390/rs6065019
Received: 24 January 2014 / Revised: 16 May 2014 / Accepted: 19 May 2014 / Published: 30 May 2014
The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks. View Full-Text
Keywords: agriculture; ASTER satellite images; object-oriented image analysis; hierarchical classification; neural networks agriculture; ASTER satellite images; object-oriented image analysis; hierarchical classification; neural networks
MDPI and ACS Style

Peña, J.M.; Gutiérrez, P.A.; Hervás-Martínez, C.; Six, J.; Plant, R.E.; López-Granados, F. Object-Based Image Classification of Summer Crops with Machine Learning Methods. Remote Sens. 2014, 6, 5019-5041. https://doi.org/10.3390/rs6065019

AMA Style

Peña JM, Gutiérrez PA, Hervás-Martínez C, Six J, Plant RE, López-Granados F. Object-Based Image Classification of Summer Crops with Machine Learning Methods. Remote Sensing. 2014; 6(6):5019-5041. https://doi.org/10.3390/rs6065019

Chicago/Turabian Style

Peña, José M.; Gutiérrez, Pedro A.; Hervás-Martínez, César; Six, Johan; Plant, Richard E.; López-Granados, Francisca. 2014. "Object-Based Image Classification of Summer Crops with Machine Learning Methods" Remote Sens. 6, no. 6: 5019-5041. https://doi.org/10.3390/rs6065019

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