What is maximum likelihood classification in remote sensing?

Published by Anaya Cole on

What is maximum likelihood classification in remote sensing?

Maximum likelihood classification assumes that the statistics for each class in each band are normally distributed and calculates the probability that a given pixel belongs to a specific class. Unless you select a probability threshold, all pixels are classified.

Is maximum likelihood supervised classification?

The most commonly used supervised classification is maximum likelihood classification (MLC), which assumes that each spectral class can be described by a multivariate normal distribution.

What is the training accuracy of this maximum likelihood classifier?

Classification results have shown that MLC is the robust technique and there is very less chances of misclassification. The classification accuracy has been achieved overall accuracy of 93.75%, producer accuracy 94%, user accuracy 96.09% and overall kappa accuracy 90.52%.

What is minimum distance classifier?

The minimum distance classifier is used to classify unknown image data to classes which minimize the distance between the image data and the class in multi-feature space. The distance is defined as an index of similarity so that the minimum distance is identical to the maximum similarity.

What is ISO cluster unsupervised classification?

The Iso Cluster Unsupervised Classification tool automatically finds the clusters in an image and outputs a classified image. This tool is based on the Iso Cluster tool.

Which is better for image classification supervised or unsupervised classification?

In comparison to unsupervised data, the usage of training data in supervised classification yields more accurate results. This is because of the presence of reduced mixed pixels in the data collected through the supervised approach.

What is MLE used for?

Maximum likelihood estimation involves defining a likelihood function for calculating the conditional probability of observing the data sample given a probability distribution and distribution parameters. This approach can be used to search a space of possible distributions and parameters.

What is minimum distance classifier in image processing?

What is unsupervised image classification?

Unsupervised image classification is the process by which each image in a dataset is identified to be a member of one of the inherent categories present in the image collection without the use of labelled training samples.

What is Isodata in remote sensing?

The ISODATA algorithm is an iterative method that uses Euclidean distance as the similarity measure to cluster data elements into different classes.

What is better for image classification?

Unsupervised and supervised image classification are the two most common approaches. However, object-based classification has gained more popularity because it’s useful for high-resolution data.