Book Description

The goal of this research is to develop a new approach to remote sensing thematic accuracy assessment in which the spectral distances between the classes in a thematic classification are used as inputs to the error estimation process. The conceptual basis for this new approach is that the confusion of relatively spectrally different classes represents a more severe error than confusing relatively spectrally similar classes. Therefore, the accuracy estimate of a classification can be adjusted to take into account the 'spectral severities' (or misclassification costs) of the errors in that classification. The benefits of including inter-class spectral distances in the accuracy assessment process are shown in the context of the development of two new accuracy assessment measures called Spectrally Weighted Kappa (SWK) and Spectrally Weighted Fuzzy (SWF). These two new accuracy assessment methods are introduced and tested for their performance relative to current techniques. The results of this research demonstrate that inter-class spectral distances can be used effectively in accuracy assessment of thematic classifications. The SWK approach can provide information about the spectral costs of errors in a classification that is not as apparent with traditional methods. In addition, SWK provides a quantitative base for establishing weights for Weighted Kappa analysis and allows for the possibility of improving a classification during its development. The SWF method improves upon current fuzzy accuracy assessment techniques by providing a way to establish membership functions that is based on inter-class spectral distances. We have shown that the SWF method can provide fuzzy membership values that are similar to those that a well-trained human might choose. Therefore, in cases where multiple interpreters would normally have been used to create fuzzy membership values, the SWF method can be employed reduce inter-interpreter bias. In addition, the SWF method provides a quanti.