Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management

Abstract

This paper compares of pixel- and object-based techniques for mapping wild oat weed patches in wheat fields using multi-spectral QuickBird satellite imagery for site-specific weed management. The research was conducted at two levels: (1) at the field level, on 11 and 15 individual infested wheat fields in 2006 and 2008, respectively, and (2) on a broader level, by analysing the entire 2006 and 2008 images. To evaluate the wild oat patches mapping at the field level, both pixel- and object-based image analyses were tested with six classification algorithms: Parallelepipeds (P), Mahalanobis Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Decision Tree (DT). The results showed that weed patches could be accurately detected with both analyses obtaining global accuracies between 80% and 99% for most of the fields. The MD and SVM classifiers were the most accurate for both the pixel- and object-based images from 2006 and 2008, respectively. In the broad-scale analysis, all of the wheat fields were identified in the imagery using a multiresolution hierarchical segmentation based on two scales. The first segmentation scale was classified using the MD and ML algorithms to discriminate wheat fields from other land uses. Accuracies greater than 85% were obtained for MD and 88% for ML for both imagery. A hierarchical analysis was then performed with the second segmentation scale, increasing the accuracies to 93% and 91% for 2006 and 2008 imagery, respectively. Finally, based on the most accurate results obtained in the field-level study, pixel-based classifications using the MD, ML and SVM algorithms were applied to the wheat fields identified. The results of these broad-level analyses showed that wild oat patches were accurately discriminated in all the wheat fields present in the entire images with accuracies greater than 91% for all the classifiers tested.