Spectral Angle Mapper¶

Module Interface¶

class torchmetrics.SpectralAngleMapper(reduction='elementwise_mean', **kwargs)[source]

The metric Spectral Angle Mapper determines the spectral similarity between image spectra and reference spectra by calculating the angle between the spectra, where small angles between indicate high similarity and high angles indicate low similarity.

As input to forward and update the metric accepts the following input

preds (Tensor): Predictions from model of shape (N,C,H,W)
target (Tensor): Ground truth values of shape (N,C,H,W)

As output of forward and compute the metric returns the following output

sam (Tensor): if reduction!='none' returns float scalar tensor with average SAM value over sample else returns tensor of shape (N,) with SAM values per sample

Parameters

reduction¶ (Literal[‘elementwise_mean’, ‘sum’, ‘none’]) –
a method to reduce metric score over labels.
- 'elementwise_mean': takes the mean (default)
- 'sum': takes the sum
- 'none' or None: no reduction will be applied
kwargs¶ (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Returns

Tensor with SpectralAngleMapper score

Example

>>> import torch
>>> from torchmetrics import SpectralAngleMapper
>>> preds = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(42))
>>> target = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(123))
>>> sam = SpectralAngleMapper()
>>> sam(preds, target)
tensor(0.5943)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface¶

torchmetrics.functional.spectral_angle_mapper(preds, target, reduction='elementwise_mean')[source]

Universal Spectral Angle Mapper.

Parameters

preds¶ (Tensor) – estimated image
target¶ (Tensor) – ground truth image
reduction¶ (Literal[‘elementwise_mean’, ‘sum’, ‘none’, None]) –
a method to reduce metric score over labels.
- 'elementwise_mean': takes the mean (default)
- 'sum': takes the sum
- 'none' or None: no reduction will be applied

Return type

Tensor

Returns

Tensor with Spectral Angle Mapper score

Raises

TypeError – If preds and target don’t have the same data type.
ValueError – If preds and target don’t have BxCxHxW shape.

Example

>>> from torchmetrics.functional import spectral_angle_mapper
>>> preds = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(42))
>>> target = torch.rand([16, 3, 16, 16], generator=torch.manual_seed(123))
>>> spectral_angle_mapper(preds, target)
tensor(0.5943)

References

[1] Roberta H. Yuhas, Alexander F. H. Goetz and Joe W. Boardman, “Discrimination among semi-arid landscape endmembers using the Spectral Angle Mapper (SAM) algorithm” in PL, Summaries of the Third Annual JPL Airborne Geoscience Workshop, vol. 1, June 1, 1992.