![]() ![]() The minimum interpretation accuracy with remotely sensed data is >=85% Accuracy of interpretation for several categories should be equal. Regroup the clusters into original classesĬriteria for USGS Landuse/landcover Classification System 1. Unsupervised classification to identify spectral clusters within the training sets. Hybrid Classification: It takes the advantage of both the supervised classification and unsupervised classification. It is the users’ responsibility to assign a class label to each of the clusters. Unsupervised classification: Instead of providing the computer with examples of features in multi-dimensional feature space, the users let the computer to identify pre-specified number of spectral clusters among which the difference between clusters are maximized and within clusters are minimized. The computer will first analyze the statistical parameters for the training data and then assign all other pixels to one of the classes in the examples based on statistical similarity. Computer-Aided Classification Parametric: assumes normal distribution of the data Supervised classification: provide the computer with some examples of known features in multi-dimensional feature space.
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