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
Normalization of Remote Sensing Imagery for Automatic Information Extraction
KTH, School of Electrical Engineering (EES), Communication Theory.ORCID iD: 0000-0003-3054-7210
2014 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

For the time being, Remote Sensing automatized techniques are conventionally designed to be used exclusively on data captured by aparticular sensor system. This convention was only adopted after evidence suggested that, in the field, algorithms that yield great resultson data from one specific satellite or sensor, tend to underachieve on data from similar sensors. With this effect in mind, we will refer to remote sensing imagery as heterogeneous.There have been attempts to compensate every effect on the data and obtain the underlying physical property that carries the information, the ground reflectance. Because of their improvement of the informative value of each image, some of them have even been standardized as common preprocessing methods. However, these techniques generally require further knowledge on certain atmospheric properties at the time the data was captured. This information is generally not available and has to be estimated or guessed by experts, avery time consuming, inaccurate and expensive task. Moreover, even if the results do improve in each of the treated images, a significant decrease of their heterogeneity is not achieved. There have been more automatized proposals to treat the data in the literature, which have been broadly named RRN (Relative Radiometric Normalization) algorithms. These consider the problem of heterogeneity itself and use properties strictly related to the statistics of remote sensing imagery to solve it. In this master thesis, an automatic algorithm to reduce heterogeneity in generic imagery is designed, characterized and evaluated through crossed classification results on remote sensing imagery.

Place, publisher, year, edition, pages
2014. , 79 p.
Series
EES Examensarbete / Master Thesis, XR-EE-KT 2014:004
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-144032OAI: oai:DiVA.org:kth-144032DiVA: diva2:710183
External cooperation
UPF, Barcelona
Educational program
Master of Science in Engineering - Electrical Engineering
Supervisors
Examiners
Available from: 2014-04-15 Created: 2014-04-04 Last updated: 2015-05-08Bibliographically approved

Open Access in DiVA

pol14.pdf(1287 kB)156 downloads
File information
File name FULLTEXT01.pdfFile size 1287 kBChecksum SHA-512
70c234d2a9950c2030da1308a6b1d9751c979e71c67c10f3ae56d67a4b661badb4ff309ab1f4150b96d97574ae452e27ece6063f049c68a1a566d600f6c60576
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
del Aguila Pla, Pol
By organisation
Communication Theory
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 156 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: 477 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