Classification of LiDar Data Using Window-Based Techniques

Classification of LiDar Data Using Window-Based Techniques

Given LiDAR maps, we focus on identifying anthropologically relevant ditches automatically on the map. Archeologists can identify these features visually at the site, but approaches based on remotely sensed data would be preferable. This paper proposes an algorithm that uses window-based technique t...

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Main Author: Li, Shuhang
Format: Others
Published: North Dakota State University 2018
Online Access:https://hdl.handle.net/10365/28064
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spelling ndltd-ndsu.edu-oai-library.ndsu.edu-10365-280642021-09-28T17:11:37Z Classification of LiDar Data Using Window-Based Techniques Li, Shuhang Given LiDAR maps, we focus on identifying anthropologically relevant ditches automatically on the map. Archeologists can identify these features visually at the site, but approaches based on remotely sensed data would be preferable. This paper proposes an algorithm that uses window-based technique to read the characteristics of each region from maps, whose ditches are already identified, regressively, and then builds histograms to represent the different characters of each region. A classification model is then built based on the histograms and used to predict future data. The goal is to produce a large training data set using window-based technology and use it to classify future data. We demonstrated our algorithm successfully identifies target regions efficiently on real LiDAR maps. National Science Foundation through grants PFI-1114363 and IIA-1355466 2018-04-30T19:27:23Z 2018-04-30T19:27:23Z 2016 text/thesis https://hdl.handle.net/10365/28064 NDSU Policy 190.6.2 https://www.ndsu.edu/fileadmin/policy/190.pdf application/pdf North Dakota State University
collection NDLTD
format Others
sources NDLTD
description Given LiDAR maps, we focus on identifying anthropologically relevant ditches automatically on the map. Archeologists can identify these features visually at the site, but approaches based on remotely sensed data would be preferable. This paper proposes an algorithm that uses window-based technique to read the characteristics of each region from maps, whose ditches are already identified, regressively, and then builds histograms to represent the different characters of each region. A classification model is then built based on the histograms and used to predict future data. The goal is to produce a large training data set using window-based technology and use it to classify future data. We demonstrated our algorithm successfully identifies target regions efficiently on real LiDAR maps. === National Science Foundation through grants PFI-1114363 and IIA-1355466
author Li, Shuhang
spellingShingle Li, Shuhang
Classification of LiDar Data Using Window-Based Techniques
author_facet Li, Shuhang
author_sort Li, Shuhang
title Classification of LiDar Data Using Window-Based Techniques
title_short Classification of LiDar Data Using Window-Based Techniques
title_full Classification of LiDar Data Using Window-Based Techniques
title_fullStr Classification of LiDar Data Using Window-Based Techniques
title_full_unstemmed Classification of LiDar Data Using Window-Based Techniques
title_sort classification of lidar data using window-based techniques
publisher North Dakota State University
publishDate 2018
url https://hdl.handle.net/10365/28064
work_keys_str_mv AT lishuhang classificationoflidardatausingwindowbasedtechniques
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