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 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.
- 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.
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.