Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Object Detection in Domain Specific Stereo-Analysed Satellite Images
Linköping University, Department of Electrical Engineering, Computer Vision.
Linköping University, Department of Electrical Engineering, Computer Vision.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more conventional with different types of Convolutional Neural Networks. A segmentation network was used as well as a few detection networks and different fusions between these. The Convolutional Neural Network approach proved to be the better of the two in terms of precision and recall but the clustering approach was not far behind. This work was done using a relatively small amount of data which potentially could have impacted the results of the Machine Learning models in a negative way.

Place, publisher, year, edition, pages
2019. , p. 100
Keywords [en]
object detection, object classification, clustering, hierarchical clustering, object localisation, machine learning, ai, image localisation, image segmentation, semantic segmentation, remote sensing images, satellite images, domain knowledge, support vector machines, svm, convolutional neural network, cnn, fully convolutional network, fcn, region-based convolutional neural network, you only look once, yolo, network fusion
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-159917ISRN: LiTH-ISY-EX--19/5254--SEOAI: oai:DiVA.org:liu-159917DiVA, id: diva2:1346426
External cooperation
Saab Dynamics AB
Subject / course
Computer Engineering
Supervisors
Examiners
Available from: 2019-08-30 Created: 2019-08-27 Last updated: 2019-08-30Bibliographically approved

Open Access in DiVA

fulltext(15839 kB)32 downloads
File information
File name FULLTEXT01.pdfFile size 15839 kBChecksum SHA-512
6859e797cdc8239296f6f3567b04ea3f04cf9781a982911ffa6500fa7baeee15f74946018b614abfe307130787f6fa5e38a7b2afd16ef211aa7c4984f4629b3c
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Grahn, FredrikNilsson, Kristian
By organisation
Computer Vision
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 32 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 138 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf