Minimum Distance Classification in Remote Sensing.
Six supervised classification methods were examined in this study for selecting optimum classifiers to identify contaminants on the surface of broiler carcasses: parallelepiped, minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper, and binary encoding classifier.
Abstract: This paper elaborates on all kinds of existing methods of computing profile error, on account of integrated comparison of all good and bad points, in the context of consistent benchmark for testing and processing, forming the definition of profile error, the article also analyses and proposes a new algorithm which is counting the minimum distance of two profile lines which are least.
Maximum likelihood classification. The real classification of the satellite images takes places with the help of extensive classification algorithms, such as for example maximum likelihood, minimum distance, cubic procedures (parallelepiped), or hierarchical classification. The most common is the maximum likelihood classification.It touches a probability density function, meaning, the.
Minimum-distance estimation (MDE) is a conceptual method for fitting a statistical model to data, usually the empirical distribution.Often-used estimators such as ordinary least squares can be thought of as special cases of minimum-distance estimation. While consistent and asymptotically normal, minimum-distance estimators are generally not statistically efficient when compared to maximum.
The main problem of parametric classification (e.g., maximum likelihood, minimum distance, etc.), is its dependence on the statistical distribution of the data (e.g. Gaussian normal distribution). So, non-parametric methods, such as Support Vector Machine (SVM) are those that have been used for classification of satellite images.
Maximum Likelihood is a method for the inference of phylogeny. It evaluates a hypothesis about evolutionary history in terms of the probability that the proposed model and the hypothesized history would give rise to the observed data set.
Minimum Distance: Uses the mean vectors for each class and calculates the Euclidean distance from each unknown pixel to the mean vector for each class. The pixels are classified to the nearest class. Mahalanobis Distance: A direction-sensitive distance classifier that uses statistics for each class. It is similar to maximum likelihood.