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

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.

Bibliographic Details

Main Authors:	Peña, José María, Gutiérrez, Pedro Antonio, Hervás-Martínez, César, Six, Johan, Plant, Richard E., López Granados, Francisca
Other Authors:	Ministerio de Ciencia y Tecnología (España)
Format:	artículo biblioteca
Published:	Multidisciplinary Digital Publishing Institute 2014-05-30
Subjects:	Agriculture, Hierarchical classification, Neural networks, ASTER satellite images, Object-oriented image analysis,
Online Access:	http://hdl.handle.net/10261/127906 http://dx.doi.org/10.13039/501100006280 http://dx.doi.org/10.13039/100005595 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100004837 http://dx.doi.org/10.13039/501100003339 http://dx.doi.org/10.13039/501100011011
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