Técnicas de procesamiento

Inteligencia Artificial y algoritmos

Técnicas para el análisis y procesamiento de imágenes aplicando métodos de machine y deep learning

AI methods for image fusion

Diverse methods exploiting the statistical and geometrical characteristics of a high resolution image, to be applied to a coarser image in order to obtain an enhanced version. The methods considered include PCA extension, arithmetic algorithm, high pass filters, neuronal networks, multirresolution analysis, etc

Neural networks for data pattern detection and prediction systems

Self-Organizing Maps (SOM) is an unsupervised machine learning technique used for pattern recognition, data reduction and clustering in collections of large data sets.

SOMs is based in a Neural Network architecture. Applications include the extraction of coherent structures in fludi flows (i.e oceanic and atmospheric flows), physical and biological geographicregionalizations, phenology studies, remote sensing data gap filling, and ocean current forecast.

AI methods to estimate water masses


Unsupervised learning algorithms are used to determine the volume of water in a river dam. The method includes the determination of a mask of water-covered pixels and an estimation of the bathymetry, in order to estimate the actual volume.

Estimation of coastal bathymetry with optical imagery

Satellite visible images can be used to determine the bathymetry close to the coast and up to a few meters. Sun glint and atmospheric absorption must be corrected. Multi-scene algorithms are used to correct turbitidity. Final maps are validation with LIDAR images.

Estimation of water quality with optical imagery


Estimations of the turbidity can be used to provide water quality indices, depending on the presence of suspended matter in the water. Turbidity is assess using the spectral information of the images, relating it to the physical properties of the water column


Estimation of HAB with optical imagery

Harmful Algae Blooms (HABs) are a major concern for administrations responsible for coastal waters. Using Sentinel-2 images, new HAB indexes capable on complex waters have been developed

Object-based image analysis: precision agriculture and other disciplines


Imagery and geo-spatial data from diverse:

  • Platforms (satellite, airborne, drone)
  • Resolutions (spatial, spectral, temporal)
  • Formats (raster, vector, LIDAR, point clouds)

are combined and /or analysed with automatic algorithms to extract information from the objects in the images

Segmentation  à Classification  à Export


Characterization of wetlands using LandSat imagery

Specific products based on LandSat imagery have been developed. Images are first geometrically and radiometrically corrected, then processed and classified to obtain flood indexes, turbidity, deph and hydroperiod.