Automatic Detection of Low Passability Terrain Features in the Scandinavian Mountains
During recent years, much focus have been put on replacing time consuming manual mappingand classification tasks with automatic methods, having minimal human interaction. Now it ispossible to quickly classify land cover and terrain features covering large areas to a digital formatand with a high acc...
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KTH, Geodesi och satellitpositionering
2019
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ftkthstockholm:oai:DiVA.org:kth-254709 2023-05-15T12:59:55+02:00 Automatic Detection of Low Passability Terrain Features in the Scandinavian Mountains Ahnlén, Fredrik 2019 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254709 eng eng KTH, Geodesi och satellitpositionering TRITA-ABE-MBT 19435 http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254709 info:eu-repo/semantics/openAccess Classification Fuzzy Logic Fuzzy Inference System Automatic Mapping Terrain Features Land Cover Mapping Cartography Shrub Mire Stony Ground Klassificering Automatisk Kartering Terrängföremål Marktäcke Kartografi Videsnår Sankmark Stenig Mark Engineering and Technology Teknik och teknologier Student thesis info:eu-repo/semantics/bachelorThesis text 2019 ftkthstockholm 2022-08-11T12:38:38Z During recent years, much focus have been put on replacing time consuming manual mappingand classification tasks with automatic methods, having minimal human interaction. Now it ispossible to quickly classify land cover and terrain features covering large areas to a digital formatand with a high accuracy. This can be achieved using nothing but remote sensing techniques,which provide a far more sustainable process and product. Still, some terrain features do not havean established methodology for high quality automatic mapping.The Scandinavian Mountains contain several terrain features with low passability, such asmires, shrub and stony ground. It would be of interest to anyone passing the land to avoid theseareas. However, they are not sufficiently mapped in current map products.The aim of this thesis was to find a methodology to classify and map these terrain featuresin the Scandinavian Mountains with high accuracy and minimal human interaction, using remotesensing techniques. The study area chosen for the analysis is a large valley and mountain sidesouth-east of the small town Abisko in northern Sweden, which contain clearly visible samplesof the targeted terrain features. The methodology was based on training a Fuzzy Logic classifierusing labeled training samples and descriptors derived from ortophotos, LiDAR data and currentmap products, chosen to separate the classes from each other by their characteristics. Firstly,a set of candidate descriptors were chosen, from which the final descriptors were obtained byimplementing a Fisher score filter. Secondly a Fuzzy Inference System was constructed usinglabeled training data from the descriptors, created by the user. Finally the entire study area wasclassified pixel-by-pixel by using the trained classifier and a majority filter was used to cluster theoutputs. The result was validated by visual inspection, comparison to the current map productsand by constructing Confusion Matrices, both for the training data and validation samples as wellas for the clustered- and ... Bachelor Thesis Abisko Northern Sweden Royal Institute of Technology, Stockholm: KTHs Publication Database DiVA Abisko ENVELOPE(18.829,18.829,68.349,68.349) |
institution |
Open Polar |
collection |
Royal Institute of Technology, Stockholm: KTHs Publication Database DiVA |
op_collection_id |
ftkthstockholm |
language |
English |
topic |
Classification Fuzzy Logic Fuzzy Inference System Automatic Mapping Terrain Features Land Cover Mapping Cartography Shrub Mire Stony Ground Klassificering Automatisk Kartering Terrängföremål Marktäcke Kartografi Videsnår Sankmark Stenig Mark Engineering and Technology Teknik och teknologier |
spellingShingle |
Classification Fuzzy Logic Fuzzy Inference System Automatic Mapping Terrain Features Land Cover Mapping Cartography Shrub Mire Stony Ground Klassificering Automatisk Kartering Terrängföremål Marktäcke Kartografi Videsnår Sankmark Stenig Mark Engineering and Technology Teknik och teknologier Ahnlén, Fredrik Automatic Detection of Low Passability Terrain Features in the Scandinavian Mountains |
topic_facet |
Classification Fuzzy Logic Fuzzy Inference System Automatic Mapping Terrain Features Land Cover Mapping Cartography Shrub Mire Stony Ground Klassificering Automatisk Kartering Terrängföremål Marktäcke Kartografi Videsnår Sankmark Stenig Mark Engineering and Technology Teknik och teknologier |
description |
During recent years, much focus have been put on replacing time consuming manual mappingand classification tasks with automatic methods, having minimal human interaction. Now it ispossible to quickly classify land cover and terrain features covering large areas to a digital formatand with a high accuracy. This can be achieved using nothing but remote sensing techniques,which provide a far more sustainable process and product. Still, some terrain features do not havean established methodology for high quality automatic mapping.The Scandinavian Mountains contain several terrain features with low passability, such asmires, shrub and stony ground. It would be of interest to anyone passing the land to avoid theseareas. However, they are not sufficiently mapped in current map products.The aim of this thesis was to find a methodology to classify and map these terrain featuresin the Scandinavian Mountains with high accuracy and minimal human interaction, using remotesensing techniques. The study area chosen for the analysis is a large valley and mountain sidesouth-east of the small town Abisko in northern Sweden, which contain clearly visible samplesof the targeted terrain features. The methodology was based on training a Fuzzy Logic classifierusing labeled training samples and descriptors derived from ortophotos, LiDAR data and currentmap products, chosen to separate the classes from each other by their characteristics. Firstly,a set of candidate descriptors were chosen, from which the final descriptors were obtained byimplementing a Fisher score filter. Secondly a Fuzzy Inference System was constructed usinglabeled training data from the descriptors, created by the user. Finally the entire study area wasclassified pixel-by-pixel by using the trained classifier and a majority filter was used to cluster theoutputs. The result was validated by visual inspection, comparison to the current map productsand by constructing Confusion Matrices, both for the training data and validation samples as wellas for the clustered- and ... |
format |
Bachelor Thesis |
author |
Ahnlén, Fredrik |
author_facet |
Ahnlén, Fredrik |
author_sort |
Ahnlén, Fredrik |
title |
Automatic Detection of Low Passability Terrain Features in the Scandinavian Mountains |
title_short |
Automatic Detection of Low Passability Terrain Features in the Scandinavian Mountains |
title_full |
Automatic Detection of Low Passability Terrain Features in the Scandinavian Mountains |
title_fullStr |
Automatic Detection of Low Passability Terrain Features in the Scandinavian Mountains |
title_full_unstemmed |
Automatic Detection of Low Passability Terrain Features in the Scandinavian Mountains |
title_sort |
automatic detection of low passability terrain features in the scandinavian mountains |
publisher |
KTH, Geodesi och satellitpositionering |
publishDate |
2019 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254709 |
long_lat |
ENVELOPE(18.829,18.829,68.349,68.349) |
geographic |
Abisko |
geographic_facet |
Abisko |
genre |
Abisko Northern Sweden |
genre_facet |
Abisko Northern Sweden |
op_relation |
TRITA-ABE-MBT 19435 http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254709 |
op_rights |
info:eu-repo/semantics/openAccess |
_version_ |
1766136170720264192 |