Generating land-cover maps from remotely sensed data: manual vectorization versus object-oriented automation

Created by Machala, M., Honzová, M. & Klimánek, M.

Posted by Ray Wyatt on September 30, 2015 | 0 comments

Manual vectorization of multispectral images is a widely used
method for making land-use or land-cover maps. Although it is usually
considered relatively accurate it is very time consuming, which has
prompted the use in recent years of various semiautomatic methods for
classifying remotely sensed images. One of the most promising of the
latter is object-oriented image analysis based upon image segmentation,
but the accuracy of its results, as well as its time demands, are disputed.
Accordingly, this paper compared manual vectorization with object-oriented
classification to reveal the strong and weak points of each. Two qualitatively
different datasets were classified using both methods; time costs were
monitored and accuracy levels were compared. It was found that manual
vectorization achieved better overall accuracy (up to 93% versus 84%), but
the semiautomatic method was usually more accurate when classifying
some specific features such as roads, built-up areas, broadleaf trees and
coniferous trees. The verdict regarding time-efficiency was less clear cut.
The best method depends upon the spatial and spectral resolution of the
data being processed.

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