Sentence examples for maximum likelihood classification from inspiring English sources

Second, maximum likelihood classification (MLC) was executed with two different band selections.

The algorithm uses maximum likelihood classification combined with a certainty based fusion criterion.

MLC (maximum likelihood classification) and SVM (support vector machine) are implemented for image classification.

We manipulated the images in ArcGIS 10.2, where the Maximum Likelihood Classification tool was used to convert them into rasters of forest presence (1) or absence (0).

Firstly, the Maximum Likelihood Classification (MLC) was applied using five different approaches combining R, G, B, NIR, and panchromatic bands.

A Maximum Likelihood Classification was used to map three types of land cover: agriculture, built-up and plantation forest.

The images were spatially subset, clouds were removed, and a supervised maximum likelihood classification was run on both images to identify change in land classes over time.

The result of maximum likelihood classification (MLC) was used to compare with the result of the classification based on the rough set theory.

The accuracies of the rough set method and maximum likelihood classification were compared, yielding overall accuracies of 94.15% and 93.88%, respectively.

First, we use a noise reduction filter using mathematical morphology, and next we use a classification algorithm such as the maximum likelihood classification method with the filtered image.

Past changes in area and patch fragmentation of land cover classes and individual forest patches in the Gamo Highlands, Ethiopia, were assessed using maximum-likelihood classification of LANDSAT images.