HySpecNet-11k#

class torchgeo.datasets.HySpecNet11k(root='data', split='train', strategy='easy', bands=None, transforms=None, download=False, checksum=False)[source]#

Bases: NonGeoDataset

HySpecNet-11k dataset.

HySpecNet-11k is a large-scale benchmark dataset for hyperspectral image compression and self-supervised learning. It is made up of 11,483 nonoverlapping image patches acquired by the EnMAP satellite. Each patch is a portion of 128 x 128 pixels with 224 spectral bands and with a ground sample distance of 30 m.

To construct HySpecNet-11k, a total of 250 EnMAP tiles acquired during the routine operation phase between 2 November 2022 and 9 November 2022 were considered. The considered tiles are associated with less than 10% cloud and snow cover. The tiles were radiometrically, geometrically and atmospherically corrected (L2A water & land product). Then, the tiles were divided into nonoverlapping image patches. The cropped patches at the borders of the tiles were eliminated. As a result, more than 45 patches per tile are obtained, resulting in 11,483 patches for the full dataset.

We provide predefined splits obtained by randomly dividing HySpecNet into:

  1. a training set that includes 70% of the patches,

  2. a validation set that includes 20% of the patches, and

  3. a test set that includes 10% of the patches.

Depending on the way that we used for splitting the dataset, we define two different splits:

  1. an easy split, where patches from the same tile can be present in different sets (patchwise splitting); and

  2. a hard split, where all patches from one tile belong to the same set (tilewise splitting).

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

Added in version 0.7.

all_bands: tuple[str, ...] = ('B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10', 'B11', 'B12', 'B13', 'B14', 'B15', 'B16', 'B17', 'B18', 'B19', 'B20', 'B21', 'B22', 'B23', 'B24', 'B25', 'B26', 'B27', 'B28', 'B29', 'B30', 'B31', 'B32', 'B33', 'B34', 'B35', 'B36', 'B37', 'B38', 'B39', 'B40', 'B41', 'B42', 'B43', 'B44', 'B45', 'B46', 'B47', 'B48', 'B49', 'B50', 'B51', 'B52', 'B53', 'B54', 'B55', 'B56', 'B57', 'B58', 'B59', 'B60', 'B61', 'B62', 'B63', 'B64', 'B65', 'B66', 'B67', 'B68', 'B69', 'B70', 'B71', 'B72', 'B73', 'B74', 'B75', 'B76', 'B77', 'B78', 'B79', 'B80', 'B81', 'B82', 'B83', 'B84', 'B85', 'B86', 'B87', 'B88', 'B89', 'B90', 'B91', 'B92', 'B93', 'B94', 'B95', 'B96', 'B97', 'B98', 'B99', 'B100', 'B101', 'B102', 'B103', 'B104', 'B105', 'B106', 'B107', 'B108', 'B109', 'B110', 'B111', 'B112', 'B113', 'B114', 'B115', 'B116', 'B117', 'B118', 'B119', 'B120', 'B121', 'B122', 'B123', 'B124', 'B125', 'B126', 'B127', 'B128', 'B129', 'B130', 'B131', 'B132', 'B133', 'B134', 'B135', 'B136', 'B137', 'B138', 'B139', 'B140', 'B141', 'B142', 'B143', 'B144', 'B145', 'B146', 'B147', 'B148', 'B149', 'B150', 'B151', 'B152', 'B153', 'B154', 'B155', 'B156', 'B157', 'B158', 'B159', 'B160', 'B161', 'B162', 'B163', 'B164', 'B165', 'B166', 'B167', 'B168', 'B169', 'B170', 'B171', 'B172', 'B173', 'B174', 'B175', 'B176', 'B177', 'B178', 'B179', 'B180', 'B181', 'B182', 'B183', 'B184', 'B185', 'B186', 'B187', 'B188', 'B189', 'B190', 'B191', 'B192', 'B193', 'B194', 'B195', 'B196', 'B197', 'B198', 'B199', 'B200', 'B201', 'B202', 'B203', 'B204', 'B205', 'B206', 'B207', 'B208', 'B209', 'B210', 'B211', 'B212', 'B213', 'B214', 'B215', 'B216', 'B217', 'B218', 'B219', 'B220', 'B221', 'B222', 'B223', 'B224')#

Names of all available bands in the dataset

rgb_bands: tuple[str, ...] = ('B48', 'B30', 'B16')#

Names of RGB bands in the dataset

__init__(root='data', split='train', strategy='easy', bands=None, transforms=None, download=False, checksum=False)[source]#

Initialize a new HySpecNet11k instance.

Parameters:

  • root (str | PathLike[str]) – Root directory where dataset can be found.

  • split (Literal['train', 'val', 'test']) – One of ‘train’, ‘val’, or ‘test’.

  • strategy (Literal['easy', 'hard']) – Either ‘easy’ for patchwise splitting or ‘hard’ for tilewise splitting.

  • bands (Sequence[str] | None) – Bands to return.

  • transforms (Callable[[dict[str, Any]], dict[str, Any]] | None) – A function/transform that takes input sample and its target as entry and returns a transformed version.

  • download (bool) – If True, download dataset and store it in the root directory.

  • checksum (bool) – If True, check the MD5 of the downloaded files (may be slow).

Raises:

DatasetNotFoundError – If dataset is not found and download is False.

__len__()[source]#

Return the number of data points in the dataset.

Returns:

Length of the dataset.

Return type:

int

__getitem__(index)[source]#

Return an index within the dataset.

Parameters:

index (int) – Index to return.

Returns:

Data and label at that index.

Return type:

dict[str, Any]

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

Plot a sample from the dataset.

Parameters:
Returns:

A matplotlib Figure with the rendered sample.

Raises:

RGBBandsMissingError – If bands does not include all RGB bands.

Return type:

Figure

__annotate_func__()#

The type of the None singleton.