Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of ESA Living Planet Symposium |
| Publisher | European Space Agency ESA |
| ISBN (Print) | 978-9-2922-1286-5 |
| Publication status | Published - 2013 |
| MoE publication type | Not Eligible |
| Event | ESA Living Planet Symposium 2013 - Edinburgh, United Kingdom Duration: 9 Sep 2013 → 13 Sep 2013 Conference number: ESA SP-722 |
Conference
| Conference | ESA Living Planet Symposium 2013 |
|---|---|
| Country | United Kingdom |
| City | Edinburgh |
| Period | 9/09/13 → 13/09/13 |
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Keywords
- time series
- change detection
Cite this
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Change detection from satellite data time series using pixel value distributions. / Saarela, Olli; Molinier, Matthieu; Sirro, Laura.
Proceedings of ESA Living Planet Symposium. European Space Agency ESA, 2013.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Change detection from satellite data time series using pixel value distributions
AU - Saarela, Olli
AU - Molinier, Matthieu
AU - Sirro, Laura
N1 - Project code: 40736
PY - 2013
Y1 - 2013
N2 - The paper proposes a novel approach for change detection from image time series. In this approach changes are detected from evaluated distances between the (possibly multivariate) distributions of pixel values. Basing change detection on these distributions facilitates, e.g., joint analysis of images having different resolutions and comparisons of smaller areas against larger images. Furthermore, clouded areas can be excluded from each image separately, allowing the data in the remaining pixels to be utilized independent on the whether the corresponding pixels have been covered by clouds in the other images in the time series. In the paper the proposed method is applied to forest cover change detection using Landsat data covering Mexico.
AB - The paper proposes a novel approach for change detection from image time series. In this approach changes are detected from evaluated distances between the (possibly multivariate) distributions of pixel values. Basing change detection on these distributions facilitates, e.g., joint analysis of images having different resolutions and comparisons of smaller areas against larger images. Furthermore, clouded areas can be excluded from each image separately, allowing the data in the remaining pixels to be utilized independent on the whether the corresponding pixels have been covered by clouds in the other images in the time series. In the paper the proposed method is applied to forest cover change detection using Landsat data covering Mexico.
KW - time series
KW - change detection
M3 - Conference article in proceedings
SN - 978-9-2922-1286-5
BT - Proceedings of ESA Living Planet Symposium
PB - European Space Agency ESA
ER -