Error Relative Global Dim. Synthesis (ERGAS)¶
Module Interface¶
- class torchmetrics.image.ergas.ErrorRelativeGlobalDimensionlessSynthesis(ratio=4, reduction='elementwise_mean', **kwargs)[source]
Calculates 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
andupdate
the metric accepts the following inputAs output of forward and compute the metric returns the following output
ergas
(Tensor
): ifreduction!='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 resolutionreduction¶ (
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'
orNone
: 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
- Return type
- Returns
Tensor with RelativeG score
- Raises
TypeError – If
preds
andtarget
don’t have the same data type.ValueError – If
preds
andtarget
don’t haveBxCxHxW 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.