Uncertainty in Remote Sensing and GIS

Introduction Uncertainty has been an important topic in geographical information science for over a decade (e.g. Goodchild and Gopal, 1989; Veregin, 1996; Kiiveri, 1997; Heu-velink, 1998; Zhang and Goodchild, 2002). This focus has led researchers to recommend over many years that the spatial output of a geographical information system (GIS) should be (at least) twofold: […]

Uncertainty in Remote Sensing and GIS : Fundamentals Part 2

Accuracy Assessment The methods discussed so far for predicting the prediction error (e.g. SE of the mean) have been model-based. Thus, they depend on model choice (the particular model chosen and fitted). Approaches for assessing the accuracy of predictions based primarily on data and, in particular, an ‘independent’ testing data set (i.e. data not used […]

Uncertainty in Remote Sensing

Introduction The goal of remote sensing is to infer information about objects from measurements made from a remote location, frequently from space. The inference process is always less than perfect and thus there is an element of uncertainty regarding the results produced using remote sensing. When viewed from this perspective, the problem of uncertainty is […]

Toward a Comprehensive View of Uncertainty in Remote Sensing Analysis

Introduction The topic of uncertainty has been receiving increasing attention in the geographical sciences (Kundzewicz, 1995; Mowrer and Congalton, 2000; Hunsaker et al., 2001; Odeh and McBratney, 2001). One might expect that an outgrowth of an increasingly mature science is a thorough quantification of uncertainty and its eventual reduction. However, if the scientific objective is […]

On the Ambiguity Induced by a Remote Sensor’s PSF Part 1

Introduction Remote sensors, whether carried by aircraft or satellites, are widely used to provide synoptic information about large areas of the Earth’s surface. As an information source, they are particularly attractive because they can acquire data for a large ground area almost instantaneously, regardless of geographic accessibility. The information they acquire has a wide range […]

On the Ambiguity Induced by a Remote Sensor’s PSF Part 2

A Computational Analysis of the Ambiguity Induced by the PSF This section describes an efficient computational technique for estimating bounds on the ambiguity induced by a sensor’s PSF. The technique can, in principle, estimate the bounds to arbitrary accuracy, and is limited in practice only by the computer power available to it. It works by […]

On the Ambiguity Induced by a Remote Sensor’s PSF Part 3

Results Illustrating the Utility of the Conditional Distribution Representation The previous section introduced a new way of representing information derived from remotely sensed data. Rather than explicitly extracting the quantity of interest – in this case, the proportion of a sub-pixel’s area covered by cereal crops – the new approach extracts a probability distribution that […]

Pixel Unmixing at the Sub-pixel Scale Based on Land Cover Class Probabilities: Application to Urban Areas Part 1

Introduction Urban features (e.g. roads, buildings) often have sharp boundaries. Because of the coarse spatial resolution of most remotely sensed images relative to such features, many pixels (particularly those containing boundaries) will contain a mixture of the spectral responses from the different features.These techniques are based on the assumption that the spectral value of each […]

Pixel Unmixing at the Sub-pixel Scale Based on Land Cover Class Probabilities: Application to Urban Areas Part 2

Kappa coefficient Kappa analysis is a discrete multivariate technique used in accuracy assessment for determining statistically if one error matrix is significantly different from another (Bishop et al., 1975). The Kappa analysis has become a popular component of accuracy assessment (Hudson and Ramm, 1987; Congalton, 1991; Richards, 1993; Congalton and Green, 1999). The Kappa statistic […]

Super-resolution Land Cover Mapping from Remotely Sensed Imagery using a Hopfield Neural Network Part 1

Introduction Accurate information on land cover is required to aid the understanding and management of the environment. Land cover represents a critical biophysical variable in determining the functioning of terrestrial ecosystems in bio-geochemical cycling, hydrological processes and the interaction between surface and atmosphere (Cihlar et al., 2000). Information on land cover is central to all […]