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Pattern recognition and feature extr...

Radermacher, Matthew Jeffery.

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Pattern recognition and feature extraction using lidar-derived elevation models in GIS: A comparison between visualization techniques and automated methods for identifying prehistoric ditch-fortified sites in North Dakota.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Pattern recognition and feature extraction using lidar-derived elevation models in GIS: A comparison between visualization techniques and automated methods for identifying prehistoric ditch-fortified sites in North Dakota./
Author:	Radermacher, Matthew Jeffery.
Description:	135 p.
Notes:	Source: Masters Abstracts International, Volume: 56-02.
Contained By:	Masters Abstracts International56-02(E).
Subject:	Archaeology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10241631
ISBN:	9781369451955

Pattern recognition and feature extraction using lidar-derived elevation models in GIS: A comparison between visualization techniques and automated methods for identifying prehistoric ditch-fortified sites in North Dakota.
Radermacher, Matthew Jeffery.

Pattern recognition and feature extraction using lidar-derived elevation models in GIS: A comparison between visualization techniques and automated methods for identifying prehistoric ditch-fortified sites in North Dakota. - 135 p.

Source: Masters Abstracts International, Volume: 56-02.

Thesis (M.S.)--North Dakota State University, 2016.

As technologies advance in the fields of geology and computer science, new methods in remote sensing, including data acquisition and analyses, make it possible to accurately model diverse landscapes. Archaeological applications of these systems are becoming increasingly popular, especially in regards to site prospection and the geospatial analysis of cultural features. Different methodologies were used to identify fortified ditch features of anthropogenic origin using aerial lidar from known prehistoric sites in North Dakota. The results were compared in an attempt to develop a system aimed at detecting similar, unrecorded morphological features on the landscape. The successful development of this program will allow archaeological investigators to review topography and locate specific features on the surface that otherwise could be difficult to identify as a result of poor visibility in the field.

ISBN: 9781369451955Subjects--Topical Terms:

558412
Archaeology.

Pattern recognition and feature extraction using lidar-derived elevation models in GIS: A comparison between visualization techniques and automated methods for identifying prehistoric ditch-fortified sites in North Dakota.
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245 1 0 $a Pattern recognition and feature extraction using lidar-derived elevation models in GIS: A comparison between visualization techniques and automated methods for identifying prehistoric ditch-fortified sites in North Dakota.
300 $a 135 p.
500 $a Source: Masters Abstracts International, Volume: 56-02.
500 $a Adviser: Jeffrey T. Clark.
502 $a Thesis (M.S.)--North Dakota State University, 2016.
520 $a As technologies advance in the fields of geology and computer science, new methods in remote sensing, including data acquisition and analyses, make it possible to accurately model diverse landscapes. Archaeological applications of these systems are becoming increasingly popular, especially in regards to site prospection and the geospatial analysis of cultural features. Different methodologies were used to identify fortified ditch features of anthropogenic origin using aerial lidar from known prehistoric sites in North Dakota. The results were compared in an attempt to develop a system aimed at detecting similar, unrecorded morphological features on the landscape. The successful development of this program will allow archaeological investigators to review topography and locate specific features on the surface that otherwise could be difficult to identify as a result of poor visibility in the field.
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650 4 $a Geographic information science and geodesy. $3 2122917
650 4 $a Native American studies. $3 2122730
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