Remote Sensing - a tool for environmental observation

Remote sensing data do not always fulfil this

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have a multi-variate, normal (Gaussian) frequency distribution. Remote sensing data do not always fulfil this condition, you should check the distribution of your training data before using this classifier. The maximum likelihood classifier is based on the Bayesian decision rule. This technique allows to use so-called ‘ a priori information’ during the classification. If a certain crop grows e.g. only on a specific type of soil and a digital soil map is available, the classification can be directed by using this information by: ‘it is very unlikely that this crop will occur on other soil type than...’. Examples and procedures are described by Strahler (1980), De Jong and Riezebos (1991) and Jansen (1994).
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83 Figure 5.20 & 5.21 Equiprobability contours defined by the maximum likelihood classifier. Probability density functions defined by the maximum likelihood classifier (Lillesand & Kiefer, 1994).
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84 Only a few types of classifiers are described in these lecture notes. Many other approaches to the classification of digital images have been developed. It is not possible to state that a given classifier is ‘best’ for all circumstances, because the characteristics of each image, the conditions of the area of interest and the objectives of the study vary so greatly. Therefore, it is essential that the image analyst understands the alternative strategies for image classification in order to select the most appropriate classifier for the task at hand. Accuracy assessments Accuracy of a classification of a remotely sensed image refers to the ‘correctness’: a measure of the agreement between a standard assumed to be correct and an image classification of unknown quality. Hence, if a number of pixels is classified as deciduous forest, the end-user wants to know what the chance (or probability) is that these pixels really represents deciduous forest or pine forest or bare soil. The most widely used procedure to assess accuracy is to work with two training sets. One training set is used to classify the image, the second set is used to estimate the correctness of the classification. Such an approach requires the availability of sufficient field data. Sources of classification errors can be numerous: human errors by the assignment of classes, human errors during the field survey, errors due to the technical part of the remote sensing system e.g. striping or line drop out, due to spectral or spatial resolution of the system, non- purity of the pixels i.e. mixed pixels covering e.g. two agricultural lots do not give pure spectral signatures of land cover types, etc. Many methods are developed to assess image classification accuracy and detailed descriptions are given by Campbell (1987) and Rosenfield & Fitzpatrick- Lins (1986). Figure 5.22 An example of an error matrix
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85 The error matrix or confusion matrix is the standard form of reporting site-specific uncertainty of a classification. It identifies overall errors and misclassification for each thematic class.
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