Hyperspectral image compression with wavelet transform to remote sensing applications and geographic information systems (WAVEGIS)

The increasing availability of a great number of Remote Sensing (RS) images (multi and hyperspectral), orthophotos, etc, and their use in Geographical Information Systems (GIS), is leading to perform research of coding techniques in order to achieve appropriately compressed formats of these images to be used in RS (classification, photo interpretation, etc) and GIS (spatial analysis, etc) beyond their simple visualization. Similarly, there is a growing consensus concerning the use of watermarking techniques to ensure the integrity and the origin of source images.

The present project is aimed at exploring several techniques of image compression and watermarking, performing them in a GIS environment by means of compression formats (lossy and lossless) with the following attributes:

  • High speed of data recovering in any image area and zoom
  • Fully preservation of particular image regions where no information loss should be produced
  • In the case of lossy compression, lossless quantization of several image physical parameters such as temperature, radiance, elevation, etc
  • Fully preservation of nodata regions, which should be maintained at any compression level
  • Availability of compression of monoband and multiband (either multi or hyperspectral) images
  • To reach high compression ratios while maintaining image quality
  • Invisible watermarking able to resist image compression

Studies on the consequences of the different parameters and on the compression ratios, experimented on images of several types, will also be part of this project. Such studies will be carried out both in the visual analysis and in the digital analysis, with the goal to establish a theory for validating the coding schemes.