PRISMA#

class torchgeo.datasets.PRISMA(paths='data', crs=None, res=None, bands=None, transforms=None, cache=True, time_series=False)[source]#

Bases: RasterDataset

PRISMA dataset.

Hyperspectral Precursor and Application Mission PRISMA (PRecursore IperSpettrale della Missione Applicativa) is a medium-resolution hyperspectral imaging satellite, developed, owned, and operated by the Italian Space Agency ASI (Agenzia Spaziale Italiana). It is the successor to the discontinued HypSEO (Hyperspectral Satellite for Earth Observation) mission.

PRISMA carries two sensor instruments, the HYC (Hyperspectral Camera) module and the PAN (Panchromatic Camera) module. The HYC sensor is a prism spectrometer for two bands, VIS/NIR (Visible/Near Infrared) and NIR/SWIR (Near Infrared/Shortwave Infrared), with a total of 237 channels across both bands. Its primary mission objective is the high resolution hyperspectral imaging of land, vegetation, inner waters and coastal zones. The second sensor module, PAN, is a high resolution optical imager, and is co-registered with HYC data to allow testing of image fusion techniques.

The HYC module has a spatial resolution of 30 m and operates in two bands, a 66 channel VIS/NIR band with a spectral interval of 400-1010 nm, and a 171 channel NIR/SWIR band with a spectral interval of 920-2505 nm. It uses a pushbroom scanning technique with a swath width of 30 km, and a field of regard of 1000 km either side. The PAN module also uses a pushbroom scanning technique, with identical swath width and field of regard but spatial resolution of 5 m.

PRISMA is in a sun-synchronous orbit, with an altitude of 614 km, an inclination of 98.19° and its LTDN (Local Time on Descending Node) is at 1030 hours.

If you use this dataset in your research, please cite the following paper:

Note

PRISMA imagery is distributed as HDF5 files. However, TorchGeo does not yet have support for reprojecting and windowed reading of HDF5 files. This data loader requires you to first convert all files from HDF5 to GeoTIFF using something like this script.

Added in version 0.6.

filename_glob = 'PRS_*'#

Glob expression used to search for files.

This expression should be specific enough that it will not pick up files from other datasets. It should not include a file extension, as the dataset may be in a different file format than what it was originally downloaded as.

filename_regex = '\n        ^PRS\n        _(?P<level>[A-Z\\d]+)\n        _(?P<product>[A-Z]+)\n        (_(?P<order>[A-Z_]+))?\n        _(?P<start>\\d{14})\n        _(?P<stop>\\d{14})\n        _(?P<version>\\d{4})\n        (_(?P<valid>\\d))?\n        \\.\n    '#

Regular expression used to extract date from filename.

The expression should use named groups. The expression may contain any number of groups. The following groups are specifically searched for by the base class:

  • date: used to calculate mint and maxt for index insertion

  • start: used to calculate mint for index insertion

  • stop: used to calculate maxt for index insertion

When separate_files is True, the following additional groups are searched for to find other files:

  • band: replaced with requested band name

date_format = '%Y%m%d%H%M%S'#

Date format string used to parse date from filename.

Not used if filename_regex does not contain a date group or start and stop groups.

plot(sample, show_titles=True, suptitle=None)[source]#

Plot a sample from the dataset.

Parameters:

  • sample (dict[str, Any]) – a sample returned by RasterDataset.__getitem__()

  • show_titles (bool) – flag indicating whether to show titles above each panel

  • suptitle (str | None) – optional string to use as a suptitle

Returns:

a matplotlib Figure with the rendered sample

Return type:

Figure