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Error Relative Global Dim. Synthesis (ERGAS)

Module Interface

class torchmetrics.image.ergas.ErrorRelativeGlobalDimensionlessSynthesis(ratio=4, reduction='elementwise_mean', **kwargs)[source]

Calculate Relative dimensionless global error synthesis (ERGAS) is used to calculate the accuracy of Pan sharpened image considering normalized average error of each band of the result image (ErrorRelativeGlobalDimensionlessSynthesis).

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

  • preds (Tensor): Predictions from model

  • target (Tensor): Ground truth values

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

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

Parameters
  • ratio (Union[int, float]) – ratio of high resolution to low resolution

  • 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

  • kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> import torch
>>> from torchmetrics import ErrorRelativeGlobalDimensionlessSynthesis
>>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> ergas = ErrorRelativeGlobalDimensionlessSynthesis()
>>> torch.round(ergas(preds, target))
tensor(154.)

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

Functional Interface

torchmetrics.functional.error_relative_global_dimensionless_synthesis(preds, target, ratio=4, reduction='elementwise_mean')[source]

Erreur Relative Globale Adimensionnelle de Synthèse.

Parameters
  • preds (Tensor) – estimated image

  • target (Tensor) – ground truth image

  • ratio (Union[int, float]) – ratio of high resolution to low resolution

  • 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 RelativeG 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 error_relative_global_dimensionless_synthesis
>>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> ergds = error_relative_global_dimensionless_synthesis(preds, target)
>>> torch.round(ergds)
tensor(154.)

References

[1] Qian Du; Nicholas H. Younan; Roger King; Vijay P. Shah, “On the Performance Evaluation of Pan-Sharpening Techniques” in IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 4, pp. 518-522, 15 October 2007, doi: 10.1109/LGRS.2007.896328.

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