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Error Relative Global Dim. Synthesis (ERGAS)

Module Interface

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

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).

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.

Returns

Tensor with ErrorRelativeGlobalDimensionlessSynthesis score

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.)

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.

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

compute()[source]

Computes explained variance over state.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters
  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Return type

None

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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