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import numpy as np
# No imputations
SATMAE_MEAN = [
1370.19151926,
1184.3824625,
1120.77120066,
1136.26026392,
1263.73947144,
1645.40315151,
1846.87040806,
1762.59530783,
1972.62420416,
582.72633433,
14.77112979,
1732.16362238,
1247.91870117,
]
SATMAE_STD = [
633.15169573,
650.2842772,
712.12507725,
965.23119807,
948.9819932,
1108.06650639,
1258.36394548,
1233.1492281,
1364.38688993,
472.37967789,
14.3114637,
1310.36996126,
1087.6020813,
]
# B10 stats are imputed with B11 stats
S2A_MEAN = [
752.40087073,
884.29673756,
1144.16202635,
1297.47289228,
1624.90992062,
2194.6423161,
2422.21248945,
2517.76053101,
2581.64687018,
2645.51888987,
2368.51236873,
2368.51236873,
1805.06846033,
]
S2A_STD = [
1108.02887453,
1155.15170768,
1183.6292542,
1368.11351514,
1370.265037,
1355.55390699,
1416.51487101,
1474.78900051,
1439.3086061,
1582.28010962,
1455.52084939,
1455.52084939,
1343.48379601,
]
# No imputations
S2C_MEAN = [
1605.57504906,
1390.78157673,
1314.8729939,
1363.52445545,
1549.44374991,
2091.74883118,
2371.7172463,
2299.90463006,
2560.29504086,
830.06605044,
22.10351321,
2177.07172323,
1524.06546312,
]
S2C_STD = [
786.78685367,
850.34818441,
875.06484736,
1138.84957046,
1122.17775652,
1161.59187054,
1274.39184232,
1248.42891965,
1345.52684884,
577.31607053,
51.15431158,
1336.09932639,
1136.53823676,
]
# B1 stats are imputed with B2 stats
# B9 stats are imputed with B8A stats
# B10 stats are imputed with B11 stats
OURS_S2_MEAN = [
1395.3408730676722,
1395.3408730676722,
1338.4026921784578,
1343.09883810357,
1543.8607982512297,
2186.2022069512263,
2525.0932853316694,
2410.3377187373408,
2750.2854646886753,
2750.2854646886753,
2234.911100061487,
2234.911100061487,
1474.5311266077113,
]
OURS_S2_STD = [
917.7041440370853,
917.7041440370853,
913.2988423581528,
1092.678723527555,
1047.2206083460424,
1048.0101611156767,
1143.6903026819996,
1098.979177731649,
1204.472755085893,
1204.472755085893,
1145.9774063078878,
1145.9774063078878,
980.2429840007796,
]
OURS_S1_MEAN = [-11.728724389184965, -18.85558188024017]
OURS_S1_STD = [4.887145774840316, 5.730270320384293]
PRESTO_S1_SUBTRACT_VALUES = [-25.0, -25.0]
PRESTO_S1_DIV_VALUES = [25.0, 25.0]
PRESTO_S2_SUBTRACT_VALUES = [float(0.0)] * len(OURS_S2_MEAN)
PRESTO_S2_DIV_VALUES = [float(1e4)] * len(OURS_S2_MEAN)
# https://github.com/zhu-xlab/SSL4EO-S12/blob/main/src/benchmark/
# pretrain_ssl/datasets/SSL4EO/ssl4eo_dataset.py
S1_MEAN = [-12.54847273, -20.19237134]
S1_STD = [5.25697717, 5.91150917]
# for Prithvi, true values are ["B02", "B03", "B04", "B05", "B06", "B07"]
# for all other bands, we just copy the nearest relevant band value; Prithvi
# shouldn't be using them anyway
PRITHVI_MEAN = [
1087.0,
1087.0,
1342.0,
1433.0,
2734.0,
1958.0,
1363.0,
1363.0,
1363.0,
1363.0,
1363.0,
1363.0,
1363.0,
]
PRITHVI_STD = [
2248.0,
2248.0,
2179.0,
2178.0,
1850.0,
1242.0,
1049.0,
1049.0,
1049.0,
1049.0,
1049.0,
1049.0,
1049.0,
]
pre_computed_stats = {
"SATMAE": {"mean": SATMAE_MEAN, "std": SATMAE_STD},
"S2A": {"mean": S2A_MEAN, "std": S2A_STD},
"S2C": {"mean": S2C_MEAN, "std": S2C_STD},
"OURS": {"mean": OURS_S2_MEAN, "std": OURS_S2_STD},
"OURS_S1": {"mean": OURS_S1_MEAN, "std": OURS_S1_STD},
"S1": {"mean": S1_MEAN, "std": S1_STD},
"presto_s1": {"mean": PRESTO_S1_SUBTRACT_VALUES, "std": PRESTO_S1_DIV_VALUES},
"presto_s2": {"mean": PRESTO_S2_SUBTRACT_VALUES, "std": PRESTO_S2_DIV_VALUES},
}
s2_band_names = [
"01 - Coastal aerosol",
"02 - Blue",
"03 - Green",
"04 - Red",
"05 - Vegetation Red Edge",
"06 - Vegetation Red Edge",
"07 - Vegetation Red Edge",
"08 - NIR",
"08A - Vegetation Red Edge",
"09 - Water vapour",
"10 - SWIR - Cirrus",
"11 - SWIR",
"12 - SWIR",
]
s1_band_names = ["VV", "VH"]
def impute_normalization_stats(band_info, imputes):
# band_info is a dictionary with band names as keys and statistics (mean / std) as values
if not imputes:
return band_info
names_list = list(band_info.keys())
new_band_info = {}
for band_name in s2_band_names:
new_band_info[band_name] = {}
if band_name in names_list:
# we have the band, so use it
new_band_info[band_name] = band_info[band_name]
else:
# we don't have the band, so impute it
for impute in imputes:
src, tgt = impute
if tgt == band_name:
# we have a match!
new_band_info[band_name] = band_info[src]
break
return new_band_info
def impute_bands(image_list, names_list, imputes):
# image_list should be one np.array per band, stored in a list
# image_list and names_list should be ordered consistently!
if not imputes:
return image_list
# create a new image list by looping through and imputing where necessary
new_image_list = []
for band_name in s2_band_names:
if band_name in names_list:
# we have the band, so append it
band_idx = names_list.index(band_name)
new_image_list.append(image_list[band_idx])
else:
# we don't have the band, so impute it
for impute in imputes:
src, tgt = impute
if tgt == band_name:
# we have a match!
band_idx = names_list.index(src)
new_image_list.append(image_list[band_idx])
break
return new_image_list
def get_norm_stats(band_info):
means = []
stds = []
if len(band_info) == len(s2_band_names):
for band_name in s2_band_names:
assert band_name in band_info, f"{band_name} not found in band_info"
means.append(band_info[band_name]["mean"])
stds.append(band_info[band_name]["std"])
elif len(band_info) == len(s1_band_names):
for band_name in s1_band_names:
assert band_name in band_info, f"{band_name} not found in band_info"
means.append(band_info[band_name]["mean"])
stds.append(band_info[band_name]["std"])
else:
raise ValueError(f"Got unexpected band_info length {len(band_info)}")
return means, stds
def normalize_bands(image, norm_cfg, band_info):
if norm_cfg["type"] == "satlas":
image = image / 8160
image = np.clip(image, 0, 1)
return image
original_dtype = image.dtype
if norm_cfg["stats"] == "dataset":
means, stds = get_norm_stats(band_info)
elif norm_cfg["stats"] in pre_computed_stats.keys():
means = pre_computed_stats[norm_cfg["stats"]]["mean"]
stds = pre_computed_stats[norm_cfg["stats"]]["std"]
else:
raise f"normalization stats not found: {norm_cfg['stats']}"
means = np.array(means)
stds = np.array(stds) * norm_cfg["std_multiplier"]
if norm_cfg["type"] == "standardize":
image = (image - means) / stds
else:
min_value = means - stds
max_value = means + stds
image = (image - min_value) / (max_value - min_value)
if norm_cfg["type"] == "norm_yes_clip":
image = np.clip(image, 0, 1)
elif norm_cfg["type"] == "norm_yes_clip_int":
# same as clipping between 0 and 1 but rounds to the nearest 1/255
image = image * 255 # scale
image = np.clip(image, 0, 255).astype(np.uint8) # convert to 8-bit integers
image = image.astype(original_dtype) / 255 # back to original_dtype between 0 and 1
elif norm_cfg["type"] == "norm_no_clip":
pass
else:
raise ValueError(
f"norm type must norm_yes_clip, norm_yes_clip_int, norm_no_clip, or standardize, not {norm_cfg['type']}"
)
return image
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