Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region
Change over time between two 512 by 512 (25 m by 25 m pixels) multispectral Landsat Thematic Mapper images dated 6 June 1986 and 27 June 1988 respectively covering a forested region in northern Sweden, is here detected by means of the iteratively reweighted multivariate alteration detection (IR-MAD)...
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ftdtupubl:oai:pure.atira.dk:publications/389e5947-ede0-449a-878e-ba9b277dfc6c 2023-12-24T10:23:39+01:00 Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region Nielsen, Allan Aasbjerg Olsson, Håkan Miranda, David Suárez, Juan Crecente, Rafael 2010 https://orbit.dtu.dk/en/publications/389e5947-ede0-449a-878e-ba9b277dfc6c http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/5930/pdf/imm5930.pdf eng eng https://orbit.dtu.dk/en/publications/389e5947-ede0-449a-878e-ba9b277dfc6c urn:ISBN:978-84-693-5600-5 info:eu-repo/semantics/restrictedAccess Nielsen , A A & Olsson , H 2010 , Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region . in D Miranda , J Suárez & R Crecente (eds) , Operational tools in forestry using remote sensing techniques . pp. 167-168 , Conference on Spatial Application Tools in Forestry , Lugo, Spain , 01/01/2010 . < http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/5930/pdf/imm5930.pdf > conferenceObject 2010 ftdtupubl 2023-11-29T23:57:52Z Change over time between two 512 by 512 (25 m by 25 m pixels) multispectral Landsat Thematic Mapper images dated 6 June 1986 and 27 June 1988 respectively covering a forested region in northern Sweden, is here detected by means of the iteratively reweighted multivariate alteration detection (IR-MAD) method followed by post-processing by means of kernel maximum autocorrelation factor (kMAF) analysis. The IR-MAD method builds on an iterated version of an established method in multivariate statistics, namely canonical correlation analysis (CCA). It finds orthogonal (i.e., uncorrelated) linear combinations of the multivariate data at two time points that have maximal correlation. These linear combinations are called the canonical variates (CV) and the corresponding correlations are called the canonical correlations. There is one set of CVs for each time point. The difference between the two set of CVs represent the change between the two time points and are called the MAD variates or the MADs for short. The MAD variates are invariant to linear and affine transformations of the original data. The sum of the squared MAD variates (properly normed to unit variance) gives us change variables that will ideally follow a so-called c2 (chi-squared) distribution with p degrees of freedom for the no-change pixels; p is the number of spectral bands in the image data. Here p=6, the thermal band is excluded from the analyses. The c2 image is the basis for calculating an image of probability for no-change, i.e., the probability for finding a higher value of the c2 statistic than the one actually found. This image is the weight image in the iteration scheme mentioned above. Iterations stop when the canonical correlations stop changing. Principal component analysis (PCA) finds orthogonal (i.e., uncorrelated) linear combinations of the multivariate data that have maximal variance. A kernel version of PCA is based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis via inner products in the ... Conference Object Northern Sweden Technical University of Denmark: DTU Orbit |
institution |
Open Polar |
collection |
Technical University of Denmark: DTU Orbit |
op_collection_id |
ftdtupubl |
language |
English |
description |
Change over time between two 512 by 512 (25 m by 25 m pixels) multispectral Landsat Thematic Mapper images dated 6 June 1986 and 27 June 1988 respectively covering a forested region in northern Sweden, is here detected by means of the iteratively reweighted multivariate alteration detection (IR-MAD) method followed by post-processing by means of kernel maximum autocorrelation factor (kMAF) analysis. The IR-MAD method builds on an iterated version of an established method in multivariate statistics, namely canonical correlation analysis (CCA). It finds orthogonal (i.e., uncorrelated) linear combinations of the multivariate data at two time points that have maximal correlation. These linear combinations are called the canonical variates (CV) and the corresponding correlations are called the canonical correlations. There is one set of CVs for each time point. The difference between the two set of CVs represent the change between the two time points and are called the MAD variates or the MADs for short. The MAD variates are invariant to linear and affine transformations of the original data. The sum of the squared MAD variates (properly normed to unit variance) gives us change variables that will ideally follow a so-called c2 (chi-squared) distribution with p degrees of freedom for the no-change pixels; p is the number of spectral bands in the image data. Here p=6, the thermal band is excluded from the analyses. The c2 image is the basis for calculating an image of probability for no-change, i.e., the probability for finding a higher value of the c2 statistic than the one actually found. This image is the weight image in the iteration scheme mentioned above. Iterations stop when the canonical correlations stop changing. Principal component analysis (PCA) finds orthogonal (i.e., uncorrelated) linear combinations of the multivariate data that have maximal variance. A kernel version of PCA is based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis via inner products in the ... |
author2 |
Miranda, David Suárez, Juan Crecente, Rafael |
format |
Conference Object |
author |
Nielsen, Allan Aasbjerg Olsson, Håkan |
spellingShingle |
Nielsen, Allan Aasbjerg Olsson, Håkan Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region |
author_facet |
Nielsen, Allan Aasbjerg Olsson, Håkan |
author_sort |
Nielsen, Allan Aasbjerg |
title |
Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region |
title_short |
Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region |
title_full |
Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region |
title_fullStr |
Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region |
title_full_unstemmed |
Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region |
title_sort |
change detection by the ir-mad and kernel maf methods in landsat tm data covering a swedish forest region |
publishDate |
2010 |
url |
https://orbit.dtu.dk/en/publications/389e5947-ede0-449a-878e-ba9b277dfc6c http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/5930/pdf/imm5930.pdf |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
Nielsen , A A & Olsson , H 2010 , Change detection by the IR-MAD and kernel MAF methods in Landsat TM data covering a Swedish forest region . in D Miranda , J Suárez & R Crecente (eds) , Operational tools in forestry using remote sensing techniques . pp. 167-168 , Conference on Spatial Application Tools in Forestry , Lugo, Spain , 01/01/2010 . < http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/5930/pdf/imm5930.pdf > |
op_relation |
https://orbit.dtu.dk/en/publications/389e5947-ede0-449a-878e-ba9b277dfc6c urn:ISBN:978-84-693-5600-5 |
op_rights |
info:eu-repo/semantics/restrictedAccess |
_version_ |
1786197774294843392 |