Shortcuts

# Error Relative Global Dim. Synthesis (ERGAS)¶

## Module Interface¶

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

Calculate Relative dimensionless global error synthesis (ERGAS).

This metric is used to calculate the accuracy of Pan sharpened image considering normalized average error of each band of the result image.

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

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:

Example

>>> import torch
>>> from torchmetrics.image 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.)

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
>>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
>>> metric.update(preds, target)
>>> fig_, ax_ = metric.plot()

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.image import ErrorRelativeGlobalDimensionlessSynthesis
>>> preds = torch.rand([16, 1, 16, 16], generator=torch.manual_seed(42))
>>> target = preds * 0.75
>>> metric = ErrorRelativeGlobalDimensionlessSynthesis()
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(preds, target))
>>> fig_, ax_ = metric.plot(values)


## Functional Interface¶

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

Erreur Relative Globale Adimensionnelle de Synthèse.

Parameters:
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.image import error_relative_global_dimensionless_synthesis
>>> gen = torch.manual_seed(42)
>>> preds = torch.rand([16, 1, 16, 16], generator=gen)
>>> 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.

© Copyright Copyright (c) 2020-2023, Lightning-AI et al... Revision b57bb6d3.

Built with Sphinx using a theme provided by Read the Docs.