Abstract
For the European Union it is of great importance to be able to estimate the area of an agricultural field in order to manage its agricultural subsidy system. The area of a field can be estimated using both classification, which allocates a pixel to a single class, and decomposition, which divides a pixel between several classes. Since decomposition is better at handling mixed pixels---pixels comprising multiple classes---which are often found at field boundaries, area estimation by decomposition is expected to be more accurate. To test this hypothesis, a data-driven decomposition method was developed and applied to a series of artificial satellite images of increasing complexity. Data-driven decomposition was able to estimate the percentage of each component with an average error of only 4.4% per mixed pixel, compared to the 43.5% achieved by the standard classification method. To investigate whether data-driven decomposition also results in an improved area estimation when using real satellite images, the true area of 17 agricultural lots was determined. Compared to these data, data-driven decomposition gave equally or more accurate estimates than a similar method based on classification in 14 of the 17 cases. Furthermore, data-driven classification also showed to be better suited for handling the small boundary structures that separated the agricultural fields. These results suggest that the accuracy of data-driven decomposition is higher than that of an area estimator based on classification when dealing with agricultural fields. In addition, this thesis deals with a lot of other subjects related to the processing of mixed pixels. Among other things, new techniques were developed to detect and decompose mixed pixels, to determine class distributions from the image itself, and to deal with negative component proportions that may result from some decomposition methods. Since most of the techniques discussed do not aim specifically at agricultural fields, they can be used for other area estimation applications as well
