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Metodología universal para clasificación de sistemas de cultivo a partir de imágenes multitemporales de sensores remotos y parcelas censadas

Resumen

Tipo:
Oferta Tecnológica
Referencia:
TOES20150302002
Publicado:
24/03/2015
Caducidad:
23/03/2016
Resumen:
Un grupo de investigación español ha patentado un procedimiento original para clasificar cultivos a partir de imágenes multitemporales de sensores remotos con aplicación en agricultura y silvicultura. Este procedimiento se gestiona con un software 2.0, que lo ejecuta semiautomáticamente. Para cada zona geográfica la metodología consiste en 1) una definición de las parcelas censadas mediante archivos vectoriales en las imágenes, 2) extracción de bandas espectrales y valores medios del índice de vegetación para cada parcela e imagen, 3) conformación de una matriz de datos de la información extraída y 4) clasificación de la matriz de datos mediante árboles de decisión. Se buscan empresas interesadas en establecer acuerdos de licencia o cooperación técnica.

Details

Tittle:
Universal methodology for crop system classification from multitemporal remote sensing imagery and census parcels
Summary:
A Spanish research group has patented an original procedure to classify crops from multitemporal remote sensing images to be used in agricultural and forestry scenes. The procedure is governed by software 2.0, which executes it semi-automatically. Before developing it the methodological approaches for cropping systems classification from remote sensing images widely varied among authors and geographic area. They are looking for companies interested in license agreements or technical cooperation.
Description:
The Spanish Research Group works in precision agriculture and remote sensing (classification of plant cover and weeds; management of high-spatial resolution multi-spectral remote images in precision agriculture. They have developed an original procedure to classified crops from multitemporal remote sensing images to be used in agricultural and forestry scenes.
For each geographical area the methodology consists of: 1) a definition of census parcels through vector files in the images; 2) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; 3) the conformation of a matrix data (MD) of the extracted information; 4) the classification of MD through decision trees (DT). The procedure is also based on preliminary land-use ground-truth work in a reduced number of parcels. Crop predictive models can be used to classify unidentified parcels land uses from the same area where the images were taken to generate the model.
The procedure meets additional advantages as follows. First, the census parcel is the unit for most administrative actions and provides record for each census parcel. Second, administrations require a defined crop classification method, almost fully relying on remote sensed images automatically or semi-automatically executed, consistently reducing the ground-visit work as much as possible. Third, the predictive models for each crop/cropping system are likely to be used for the same area in subsequent years if the images were taken on about the same dates. This use is based on the true assumption that in each geographical area, the diversity of the crops and the crop calendar remain about the same throughout the years. The phenology or crop growth stages will approximately coincide, as the images were taken at about the same time in different years; therefore, the predictive models that were determined for one year with similar timings could tentatively be used in subsequent years.
They are looking for companies interested in license agreement, further development, testing of new applications or adaptation to specific needs.
Advantages and Innovations:
Cropping systems classification through conventional image processing is time consuming and requires computer language skills. However, this novel system is semi-automatically executed and faster.
The software 2.0 can be implemented for any agricultural region semi-automatically, in an economically feasible manner.
Stage of Development:
Field tested/evaluated
IPs:
Granted patent or patent application essential
CommeR Statunts Regarding IPR Status:
Patent applied for in Spain

Partner sought

Type and Role of Partner Sought:
Companies and Research Centres
Specific area of activity: Remote sensing imagery, crop classification, precision agriculture
Task to be performed: Taking photograph spectral images of land and crops through satellite or unmanned drone flights

Client

Type and Size of Client:
R&D Institution
Already Engaged in Trans-National Cooperation:
No
Languages Spoken:
English
Spanish

Keywords

Technology Keywords:
07001007 Agricultura de precisión
01003021 Control remoto
01004007 Sistemas de información geográfica (GIS)