The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Method for generating SPOT natural-colour composite images based on spectrum machine learning
Because of their high ground resolution and their ability to provide stereoscopic observation, Satellite Pour l'Observation de la Terre (SPOT) images have been widely used in the field of land-use observation, agriculture detection, forestry-resource surveying, environmental monitoring and large-scale photogrammetric mapping. However, SPOT images have no blue channel. It is therefore difficult for users to create a composite natural-colour image, which has restricted the field of application for SPOT images in areas such as fly-through of draped terrain, visual interpretation or display generation for non-remote-sensing professionals. To overcome this limitation, this article proposes a new approach for generating pseudo-natural-colour composite (NCC) representations from false-colour composite (FCC) images based on spectrum machine learning (SML). Taking samples in a spectral library as an a priori knowledge database, this article uses a machine-learning method to establish an implicit non-linear relationship between the blue band and other bands (green, red, near-infrared (NIR) and shortwave infrared (SWIR)) using a support vector machine. Then, the non-linear relationship is used on a SPOT image to simulate a new blue band. The blue band, along with the green and red bands, provides a near-true or 'natural' colour on the display. Experimental results show that the method is valid. The proposed 'natural colour generator' can be used to change false-colour images to natural-colour images. The quality of the generated pseudo-natural-colour (PNC) images is fully acceptable for manual mapping. In addition, the method can also be applied to other satellite images to simulate natural-colour images.
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