Developing improved sharpening methods for higher spectral resolution EO data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00025798%3A_____%2F16%3A00000032" target="_blank" >RIV/00025798:_____/16:00000032 - isvavai.cz</a>

Výsledek na webu

<a href="http://lps16.esa.int/page_session184.php#1692p" target="_blank" >http://lps16.esa.int/page_session184.php#1692p</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Developing improved sharpening methods for higher spectral resolution EO data

Popis výsledku v původním jazyce

The main objective of our work was to develop new approaches of the sharpening routines applied on the higher spectral resolution image data which are of a different sensor origin. This is also one of the issues in the development of optimal sharpening algorithm and will be very useful for sharpening a new generation superspectral and even hyperspectral satellite data such as Sentinel-2 and EnMap. An optimal sharpening algorithm would preserve both good spatial and spectral information, which is still not satisfactory by most of the newly developed methods, thus the research in pansharpening algorithms is still a developing field of remote sensing science. There are several traditional algorithms which have been developed and successfully used until present time, such as PCA (Principal component analysis), Gram-Schmidt, Ehlers fusion or Brovey transform. The above mentioned group of algorithms is mostly valued for its easy implementation, broad availability in most popular software and a low computational time, but have weakneses in transferring the spectral information to the sharpened image. From those reasons we adopted for our study the PCA algorithm and Gram-Schmidt principles which we further developed. Besides that we have also tested our own algorithms which were based on the Crisp sharpening algorithm (Winter et al., 2007). Both approaches led to the development of modified sharpening approaches which improved the transfer of the spectral information between the source image and the sharpened image. This is of a special interest of the geological case studies where the precise geological information is demanded. Results were validated by several traditional validation metrics, such as ERGAS, SAM, SFF, QNR or UIQI. In consequence, an approach is proposed that provides convincing results for proximal sensing as for sharpening EnMAP with Sentinel-2.

Název v anglickém jazyce

Developing improved sharpening methods for higher spectral resolution EO data

Popis výsledku anglicky

The main objective of our work was to develop new approaches of the sharpening routines applied on the higher spectral resolution image data which are of a different sensor origin. This is also one of the issues in the development of optimal sharpening algorithm and will be very useful for sharpening a new generation superspectral and even hyperspectral satellite data such as Sentinel-2 and EnMap. An optimal sharpening algorithm would preserve both good spatial and spectral information, which is still not satisfactory by most of the newly developed methods, thus the research in pansharpening algorithms is still a developing field of remote sensing science. There are several traditional algorithms which have been developed and successfully used until present time, such as PCA (Principal component analysis), Gram-Schmidt, Ehlers fusion or Brovey transform. The above mentioned group of algorithms is mostly valued for its easy implementation, broad availability in most popular software and a low computational time, but have weakneses in transferring the spectral information to the sharpened image. From those reasons we adopted for our study the PCA algorithm and Gram-Schmidt principles which we further developed. Besides that we have also tested our own algorithms which were based on the Crisp sharpening algorithm (Winter et al., 2007). Both approaches led to the development of modified sharpening approaches which improved the transfer of the spectral information between the source image and the sharpened image. This is of a special interest of the geological case studies where the precise geological information is demanded. Results were validated by several traditional validation metrics, such as ERGAS, SAM, SFF, QNR or UIQI. In consequence, an approach is proposed that provides convincing results for proximal sensing as for sharpening EnMAP with Sentinel-2.

Druh

O - Ostatní výsledky

CEP obor

JV - Kosmické technologie

OECD FORD obor

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Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů