Browsing by Author "Gilbertson, Jason Kane"
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- ItemMachine learning for object-based crop classification using multi-temporal Landsat-8 imagery(Stellenbosch : Stellenbosch University, 2017-12) Gilbertson, Jason Kane; Van Niekerk, Adriaan; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.ENGLISH ABSTRACT: Up-to-date and accurate crop maps are needed to update agricultural statistics, aid in yield forecasting, and are often used in environmental modelling. In situ methods are associated with high production costs and inefficient use of time, which hinder crop map production and reduce the usefulness of crop maps. Remote sensing offers an unbiased, cost-effective, and reliable way of mapping crops at a local, regional, and national scale. Currently, the use of multi-temporal optical imagery produces the most accurate crop maps. However, multi-temporal imagery often results in high feature dimensionality (large numbers of variables), which can negatively impact crop classification accuracy. It is therefore important to assess the benefits and limitations of using multi-temporal optical data for crop-type differentiation. This study undertakes this assessment by conducting several experiments based on multi-temporal Landsat-8 imagery in the Cape Winelands of the Western Cape, South Africa. The first experiment assessed the effect of pansharpening (image fusion), a pre-processing technique, on supervised, multi-temporal classification of crops. A suitable number of Landsat-8 images was collected based on a crop calendar of the study area. Two separate datasets, (comprising a standard resolution set of imagery and a pansharpened set of imagery) were used to create a range of image features. The images were then classified using several machine learning classifiers. Results showed that pansharpening had a significant positive influence on classification accuracy and that the support vector machine (SVM) classifier produced the most accurate results (95.9%). The second experiment utilized datasets produced in the first experiment to compare image analysis paradigms. The standard and pansharpened datasets were both segmented to produce image objects. Image object classification was then compared to the initial pixel-based classification to see which method was superior for crop differentiation with multi-temporal imagery. It was found that the object-based image analysis (OBIA) only slightly outperformed the pixel-based image analysis (PBIA), raising the question of whether the slight improvement in accuracy of the former approach is worth the effort of generating suitable image objects. In the third experiment, the capability of feature selection and feature extraction methods to mitigate high feature dimensionality were tested. Informed by the findings of the previous experiments, an OBIA approach with pansharpened imagery was used as input to feature selection and feature extraction. Results showed that feature selection did not improve the accuracy of the best performing classifier (SVM). It was concluded that feature selection is not necessary for crop differentiation when a relatively small set of features (< 200) is used. In general, multi-temporal Landsat-8 imagery shows much potential for producing accurate crop type maps. However, more research is required to evaluate the methodology in other areas and climates. Investigations into how crop type maps can be generated without collecting large numbers of training samples are also needed.