HySpecNet-11k: add additional metadata

tests/data/hyspecnet/data.py

@@ -16,8 +16,7 @@
 
 np.random.seed(0)
 
-# Tile name purposefully shortened to avoid Windows git filename length limit.
-tiles = ['ENMAP01_20221103T162438Z']
+tiles = ['ENMAP01-____L2A-DT0000004950_20221103T162438Z_001_V010110_20221118T145147Z']
 patches = ['Y01460273_X05670694', 'Y01460273_X06950822']
 
 profile = {
@@ -41,7 +40,8 @@
 for tile in tiles:
     for patch in patches:
         # Split CSV
-        path = os.path.join(tile, f'{tile}-{patch}', f'{tile}-{patch}-DATA.npy')
+        # Directory purposefully shortened to avoid Windows git filename length limit.
+        path = os.path.join(f'{tile}-{patch}-DATA.npy')
         for split in ['train', 'val', 'test']:
             with open(os.path.join(root, 'splits', 'easy', f'{split}.csv'), 'a+') as f:
                 f.write(f'{path}\n')

tests/data/hyspecnet/hyspecnet-11k/splits/easy/test.csv

@@ -1,2 +1,2 @@
-ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X05670694/ENMAP01_20221103T162438Z-Y01460273_X05670694-DATA.npy
-ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X06950822/ENMAP01_20221103T162438Z-Y01460273_X06950822-DATA.npy
+ENMAP01-____L2A-DT0000004950_20221103T162438Z_001_V010110_20221118T145147Z-Y01460273_X05670694-DATA.npy
+ENMAP01-____L2A-DT0000004950_20221103T162438Z_001_V010110_20221118T145147Z-Y01460273_X06950822-DATA.npy

tests/data/hyspecnet/hyspecnet-11k/splits/easy/train.csv

@@ -1,2 +1,2 @@
-ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X05670694/ENMAP01_20221103T162438Z-Y01460273_X05670694-DATA.npy
-ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X06950822/ENMAP01_20221103T162438Z-Y01460273_X06950822-DATA.npy
+ENMAP01-____L2A-DT0000004950_20221103T162438Z_001_V010110_20221118T145147Z-Y01460273_X05670694-DATA.npy
+ENMAP01-____L2A-DT0000004950_20221103T162438Z_001_V010110_20221118T145147Z-Y01460273_X06950822-DATA.npy

tests/data/hyspecnet/hyspecnet-11k/splits/easy/val.csv

@@ -1,2 +1,2 @@
-ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X05670694/ENMAP01_20221103T162438Z-Y01460273_X05670694-DATA.npy
-ENMAP01_20221103T162438Z/ENMAP01_20221103T162438Z-Y01460273_X06950822/ENMAP01_20221103T162438Z-Y01460273_X06950822-DATA.npy
+ENMAP01-____L2A-DT0000004950_20221103T162438Z_001_V010110_20221118T145147Z-Y01460273_X05670694-DATA.npy
+ENMAP01-____L2A-DT0000004950_20221103T162438Z_001_V010110_20221118T145147Z-Y01460273_X06950822-DATA.npy

tests/datasets/test_hyspecnet.py

@@ -52,7 +52,7 @@ def test_plot(self, dataset: HySpecNet11k) -> None:
         plt.close()
 
     def test_plot_rgb(self, dataset: HySpecNet11k) -> None:
-        dataset = HySpecNet11k(root=dataset.root, bands=(1, 2, 3))
+        dataset = HySpecNet11k(root=dataset.root, bands=('B1', 'B2', 'B3'))
         match = 'Dataset does not contain some of the RGB bands'
         with pytest.raises(RGBBandsMissingError, match=match):
             dataset.plot(dataset[0])

torchgeo/datasets/hyspecnet.py

@@ -4,6 +4,7 @@
 """HySpecNet dataset."""
 
 import os
+import re
 from collections.abc import Callable, Sequence
 from typing import ClassVar
 
@@ -14,36 +15,16 @@
 from matplotlib.figure import Figure
 from torch import Tensor
 
+from .enmap import EnMAP
 from .errors import DatasetNotFoundError, RGBBandsMissingError
 from .geo import NonGeoDataset
-from .utils import Path, download_url, extract_archive, percentile_normalization
-
-# https://git.tu-berlin.de/rsim/hyspecnet-tools/-/blob/main/tif_to_npy.ipynb
-invalid_channels = [
-    126,
-    127,
-    128,
-    129,
-    130,
-    131,
-    132,
-    133,
-    134,
-    135,
-    136,
-    137,
-    138,
-    139,
-    140,
-    160,
-    161,
-    162,
-    163,
-    164,
-    165,
-    166,
-]
-valid_channels_ids = [c + 1 for c in range(224) if c not in invalid_channels]
+from .utils import (
+    Path,
+    disambiguate_timestamp,
+    download_url,
+    extract_archive,
+    percentile_normalization,
+)
 
 
 class HySpecNet11k(NonGeoDataset):
@@ -99,15 +80,16 @@ class HySpecNet11k(NonGeoDataset):
         'hyspecnet-11k-splits.tar.gz': '94fad9e3c979c612c29a045406247d6c',
     }
 
-    all_bands = valid_channels_ids
-    rgb_bands = (43, 28, 10)
+    all_bands = EnMAP.all_bands
+    default_bands = EnMAP.default_bands
+    rgb_bands = EnMAP.rgb_bands
 
     def __init__(
         self,
         root: Path = 'data',
         split: str = 'train',
         strategy: str = 'easy',
-        bands: Sequence[int] = all_bands,
+        bands: Sequence[str] | None = None,
         transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
         download: bool = False,
         checksum: bool = False,
@@ -131,11 +113,14 @@ def __init__(
         self.root = root
         self.split = split
         self.strategy = strategy
-        self.bands = bands
+        self.bands = bands or self.default_bands
         self.transforms = transforms
         self.download = download
         self.checksum = checksum
 
+        self.wavelengths = torch.tensor([EnMAP.wavelengths[b] for b in self.bands])
+        self.band_indices = [self.all_bands.index(b) + 1 for b in self.bands]
+
         self._verify()
 
         path = os.path.join(root, 'hyspecnet-11k', 'splits', strategy, f'{split}.csv')
@@ -159,9 +144,23 @@ def __getitem__(self, index: int) -> dict[str, Tensor]:
         Returns:
             Data and label at that index.
         """
-        file = self.files[index].replace('DATA.npy', 'SPECTRAL_IMAGE.TIF')
+        path = self.files[index].replace('DATA.npy', 'SPECTRAL_IMAGE.TIF')
+        file = os.path.basename(path)
+        match = re.match(EnMAP.filename_regex, file, re.VERBOSE)
+        assert match
+        mint, maxt = disambiguate_timestamp(match.group('date'), EnMAP.date_format)
+
         with rio.open(os.path.join(self.root, 'hyspecnet-11k', 'patches', file)) as src:
-            sample = {'image': torch.tensor(src.read(self.bands).astype('float32'))}
+            minx, maxx = src.bounds.left, src.bounds.right
+            miny, maxy = src.bounds.bottom, src.bounds.top
+            sample = {
+                'image': torch.tensor(src.read(self.band_indices).astype('float32')),
+                'x': torch.tensor((minx + maxx) / 2),
+                'y': torch.tensor((miny + maxy) / 2),
+                't': torch.tensor((mint + maxt) / 2),
+                'wavelength': self.wavelengths,
+                'res': torch.tensor(30),
+            }
 
         if self.transforms is not None:
             sample = self.transforms(sample)