Assessment and visualisation of uncertainty in remote sensing land cover classifications

Abstract

The ability of space- and airborne instruments to measure the amount of electromagnetic radiation reflected and emitted by the Earths surface has proved to be valuable for the understanding of our environment, as it provides for an overwhelming flow of data on the appearance and condition of our planet. The data yielded by remote sensing can be subjected to various types of computer-assisted manipulation, to arrive at derived data sets tailored to different types of application. Computer-assisted classification of remotely sensed data into qualitative classes, for example, is useful for extracting information that can be exploited for cartographic purposes, such as in the generation of thematic maps of land cover types. For a proper cartographic application, the fitness for use of a set of remotely sensed data needs be assessed. The practicability of the data and their classification can be established by means of an accuracy assessment procedure. An error matrix is created for the classification by matching a random sample and its counterpart from a reference data set representing the actual environment. Accuracy assessment based on an error matrix, however, has several drawbacks. Among these is the non-spatial and general character of a global statement like 95% accuracy for an entire classification; moreover, accuracy assessment is a time-consuming and cost-intensive process. As a consequence, it is easily omitted which, of course, is undesirable and may lead to the use of data that are unfit for the application at hand. For assessing the fitness for use of a set of remotely sensed data, accuracy is not the only consideration. More generally, the phrase data quality is used to refer to the extent to which the characteristics of the data meet the requirements of the application aimed at by the user. A high quality indicates a relatively high information value for the considered application - a good fitness for use. Uncertainty is a key-issue in quality assessment and, therefore, in the assessment of fitness for use of a data set. During the life cycle of remotely sensed data uncertainties are introduced and propagated in an often unknown way. For investigating uncertainty, effective measures need to be designed. To this end, it is relevant to consider the purpose to which these measures are to be employed. Here, the focus is on an exploratory perspective. Exploratory analysis of a set of remotely sensed data aims at acquiring insight into the stability of various possible classifications of these data. For this purpose, knowledge about the uncertainties underlying these classifications is imperative. As in exploratory analysis, classification is an iterative process, needing not only measures for assessing the uncertainty in a classification but also effective ways to convey this information to the user. Visualisation is generally considered a useful means of communication of potentially relevant information. In this thesis a class of measures of uncertainty is presented, tailored to the purpose of exploratory analysis of remotely sensed data, together with various ways of cartographic visualisation of uncertainty. The uncertainty that is introduced during classification of a set of remotely sensed data is characterised by the probability vectors that are yielded as a by-product of most probabilistic classification procedures. Here, emphasis is laid on maximum a?x posteriori classifications where for every pixel in the data a vector of probabilities is calculated that specifies for each distinguished class its probability of being the true class. The probability vectors reflect the differences in uncertainty in the resulting classification and can be stored in a gis to serve as a basis for the derivation of weighted uncertainty measures such as entropy. Besides the assessment of uncertainty, efforts can be aimed at the reduction of the amount of uncertainty present in a remotely sensed data set. The maximum a posteriori classification rules being dealt with in this thesis allow for the introduction of a priori knowledge in the classification process, at different levels of sophistication -thereby exceeding the simple approaches embraced in existing image processing packages. Another strategy within the realm of dealing with spatial data uncertainty is based on the idea of decision analysis that allows for an optimal decision-making given uncertain information classes. Combining probability theory (defining the uncertainty related to the occurrence of a particular class) and utility theory (defining the desirability of the consequences resulting from the actions that are taken assuming that particular class) contributes to the selection of the best decision under the given conditions. This idea is particularly interesting when dealing with huge data sets under uncertain circumstances and with far-reaching consequences for wrong decisions (e.g. agricultural fraud detection by European Union). Both the probabilistic results from the classification procedure and other quality information are subjected to cartographic visualisation rules in order to develop a framework for the communication of this spatial metadata. Static as well as more dynamic approaches offer grips for the gis user who needs to consider simple but persuasive maps to assess the fitness for use of a classification. Commercial gis packages are still failing when the sound consideration of spatial data uncertainty is at stake, a fact that has incited the participants of the camotius project to look for the functionality of an uncertainty-sensitive information system. Such a system is valuable for the Dutch situation in which the extra value added by remotely sensed data is not always beyond all doubt; the explicit evaluation of these data as well as their inherent uncertainty reveals their true information value. Two case studies have stressed the role of remote sensing for planning purposes by demonstrating its ability to monitor changes in the extent of greenhouses over space and time, and making inventories of their area. The inclusion of uncertainty information allows for an exploratory approach in which an appeal can be made to several levels of knowledge in order to improve the processing results. It is stated that a user will be encouraged to use remotely sensed data if their extra value is clearly demonstrable. The components that have been scrutinised in the methodological part of this thesis are formalised in a demonstration programme that could serve as a blueprint for commercial gis packages. It can be downloaded from: http://cartography.geog.uu.nl/research/ph