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torchgeo.transforms

TorchGeo transforms.

class torchgeo.transforms.AppendBNDVI(index_nir, index_blue)[source]

Bases: AppendNormalizedDifferenceIndex

Blue Normalized Difference Vegetation Index (BNDVI).

Computes the following index:

\[\text{BNDVI} = \frac{\text{NIR} - \text{B}}{\text{NIR} + \text{B}}\]

If you use this index in your research, please cite the following paper:

New in version 0.3.

__init__(index_nir, index_blue)[source]

Initialize a new transform instance.

Parameters:
  • index_nir (int) – index of the NIR band, e.g. B8 in Sentinel 2 imagery

  • index_blue (int) – index of the Blue band, e.g. B2 in Sentinel 2 imagery

class torchgeo.transforms.AppendGBNDVI(index_nir, index_green, index_blue)[source]

Bases: AppendTriBandNormalizedDifferenceIndex

Green-Blue Normalized Difference Vegetation Index (GBNDVI).

Computes the following index:

\[\text{GBNDVI} = \frac{\text{NIR} - (\text{G} + \text{B})}{\text{NIR} + (\text{G} + \text{B})}\]

If you use this index in your research, please cite the following paper:

New in version 0.3.

__init__(index_nir, index_green, index_blue)[source]

Initialize a new transform instance.

Parameters:
  • index_nir (int) – index of the NIR band, e.g. B8 in Sentinel 2 imagery

  • index_green (int) – index of the Green band, B3 in Sentinel 2 imagery

  • index_blue (int) – index of the Blue band, B2 in Sentinel 2 imagery

class torchgeo.transforms.AppendGNDVI(index_nir, index_green)[source]

Bases: AppendNormalizedDifferenceIndex

Green Normalized Difference Vegetation Index (GNDVI).

Computes the following index:

\[\text{GNDVI} = \frac{\text{NIR} - \text{G}}{\text{NIR} + \text{G}}\]

If you use this index in your research, please cite the following paper:

New in version 0.3.

__init__(index_nir, index_green)[source]

Initialize a new transform instance.

Parameters:
  • index_nir (int) – index of the NIR band, e.g. B8 in Sentinel 2 imagery

  • index_green (int) – index of the Green band, e.g. B3 in Sentinel 2 imagery

class torchgeo.transforms.AppendGRNDVI(index_nir, index_green, index_red)[source]

Bases: AppendTriBandNormalizedDifferenceIndex

Green-Red Normalized Difference Vegetation Index (GRNDVI).

Computes the following index:

\[\text{GRNDVI} = \frac{\text{NIR} - (\text{G} + \text{R})}{\text{NIR} + (\text{G} + \text{R})}\]

If you use this index in your research, please cite the following paper:

New in version 0.3.

__init__(index_nir, index_green, index_red)[source]

Initialize a new transform instance.

Parameters:
  • index_nir (int) – index of the NIR band, e.g. B8 in Sentinel 2 imagery

  • index_green (int) – index of the Green band, B3 in Sentinel 2 imagery

  • index_red (int) – index of the Red band, B4 in Sentinel 2 imagery

class torchgeo.transforms.AppendNBR(index_nir, index_swir)[source]

Bases: AppendNormalizedDifferenceIndex

Normalized Burn Ratio (NBR).

Computes the following index:

\[\text{NBR} = \frac{\text{NIR} - \text{SWIR}}{\text{NIR} + \text{SWIR}}\]

If you use this index in your research, please cite the following paper:

New in version 0.2.

__init__(index_nir, index_swir)[source]

Initialize a new transform instance.

Parameters:
  • index_nir (int) – index of the Near Infrared (NIR) band in the image

  • index_swir (int) – index of the Short-wave Infrared (SWIR) band in the image

class torchgeo.transforms.AppendNDBI(index_swir, index_nir)[source]

Bases: AppendNormalizedDifferenceIndex

Normalized Difference Built-up Index (NDBI).

Computes the following index:

\[\text{NDBI} = \frac{\text{SWIR} - \text{NIR}}{\text{SWIR} + \text{NIR}}\]

If you use this index in your research, please cite the following paper:

__init__(index_swir, index_nir)[source]

Initialize a new transform instance.

Parameters:
  • index_swir (int) – index of the Short-wave Infrared (SWIR) band in the image

  • index_nir (int) – index of the Near Infrared (NIR) band in the image

class torchgeo.transforms.AppendNDRE(index_nir, index_vre1)[source]

Bases: AppendNormalizedDifferenceIndex

Normalized Difference Red Edge Vegetation Index (NDRE).

Computes the following index:

\[\text{NDRE} = \frac{\text{NIR} - \text{VRE1}}{\text{NIR} + \text{VRE1}}\]

If you use this index in your research, please cite the following paper:

New in version 0.3.

__init__(index_nir, index_vre1)[source]

Initialize a new transform instance.

Parameters:
  • index_nir (int) – index of the NIR band, e.g. B8 in Sentinel 2 imagery

  • index_vre1 (int) – index of the Red Edge band, B5 in Sentinel 2 imagery

class torchgeo.transforms.AppendNDSI(index_green, index_swir)[source]

Bases: AppendNormalizedDifferenceIndex

Normalized Difference Snow Index (NDSI).

Computes the following index:

\[\text{NDSI} = \frac{\text{G} - \text{SWIR}}{\text{G} + \text{SWIR}}\]

If you use this index in your research, please cite the following paper:

__init__(index_green, index_swir)[source]

Initialize a new transform instance.

Parameters:
  • index_green (int) – index of the Green band in the image

  • index_swir (int) – index of the Short-wave Infrared (SWIR) band in the image

class torchgeo.transforms.AppendNDVI(index_nir, index_red)[source]

Bases: AppendNormalizedDifferenceIndex

Normalized Difference Vegetation Index (NDVI).

Computes the following index:

\[\text{NDVI} = \frac{\text{NIR} - \text{R}}{\text{NIR} + \text{R}}\]

If you use this index in your research, please cite the following paper:

__init__(index_nir, index_red)[source]

Initialize a new transform instance.

Parameters:
  • index_nir (int) – index of the Near Infrared (NIR) band in the image

  • index_red (int) – index of the Red band in the image

class torchgeo.transforms.AppendNDWI(index_green, index_nir)[source]

Bases: AppendNormalizedDifferenceIndex

Normalized Difference Water Index (NDWI).

Computes the following index:

\[\text{NDWI} = \frac{\text{G} - \text{NIR}}{\text{G} + \text{NIR}}\]

If you use this index in your research, please cite the following paper:

__init__(index_green, index_nir)[source]

Initialize a new transform instance.

Parameters:
  • index_green (int) – index of the Green band in the image

  • index_nir (int) – index of the Near Infrared (NIR) band in the image

class torchgeo.transforms.AppendNormalizedDifferenceIndex(index_a, index_b)[source]

Bases: IntensityAugmentationBase2D

Append normalized difference index as channel to image tensor.

Computes the following index:

\[\text{NDI} = \frac{A - B}{A + B}\]

New in version 0.2.

__init__(index_a, index_b)[source]

Initialize a new transform instance.

Parameters:
  • index_a (int) – reference band channel index

  • index_b (int) – difference band channel index

apply_transform(input, params, flags, transform=None)[source]

Apply the transform.

Parameters:
Returns:

the augmented input

Return type:

Tensor

class torchgeo.transforms.AppendRBNDVI(index_nir, index_red, index_blue)[source]

Bases: AppendTriBandNormalizedDifferenceIndex

Red-Blue Normalized Difference Vegetation Index (RBNDVI).

Computes the following index:

\[\text{RBNDVI} = \frac{\text{NIR} - (\text{R} + \text{B})}{\text{NIR} + (\text{R} + \text{B})}\]

If you use this index in your research, please cite the following paper:

New in version 0.3.

__init__(index_nir, index_red, index_blue)[source]

Initialize a new transform instance.

Parameters:
  • index_nir (int) – index of the NIR band, e.g. B8 in Sentinel 2 imagery

  • index_red (int) – index of the Red band, B4 in Sentinel 2 imagery

  • index_blue (int) – index of the Blue band, B2 in Sentinel 2 imagery

class torchgeo.transforms.AppendSWI(index_vre1, index_swir2)[source]

Bases: AppendNormalizedDifferenceIndex

Standardized Water-Level Index (SWI).

Computes the following index:

\[\text{SWI} = \frac{\text{VRE1} - \text{SWIR2}}{\text{VRE1} + \text{SWIR2}}\]

If you use this index in your research, please cite the following paper:

New in version 0.3.

__init__(index_vre1, index_swir2)[source]

Initialize a new transform instance.

Parameters:
  • index_vre1 (int) – index of the VRE1 band, e.g. B5 in Sentinel 2 imagery

  • index_swir2 (int) – index of the SWIR2 band, e.g. B11 in Sentinel 2 imagery

class torchgeo.transforms.AppendTriBandNormalizedDifferenceIndex(index_a, index_b, index_c)[source]

Bases: IntensityAugmentationBase2D

Append normalized difference index involving 3 bands as channel to image tensor.

Computes the following index:

\[\text{TBNDI} = \frac{A - (B + C)}{A + (B + C)}\]

New in version 0.3.

__init__(index_a, index_b, index_c)[source]

Initialize a new transform instance.

Parameters:
  • index_a (int) – reference band channel index

  • index_b (int) – difference band channel index of component 1

  • index_c (int) – difference band channel index of component 2

apply_transform(input, params, flags, transform=None)[source]

Apply the transform.

Parameters:
Returns:

the augmented input

Return type:

Tensor

class torchgeo.transforms.AugmentationSequential(*args, data_keys, **kwargs)[source]

Bases: Module

Wrapper around kornia AugmentationSequential to handle input dicts.

Deprecated since version 0.4: Use kornia.augmentation.container.AugmentationSequential instead.

__init__(*args, data_keys, **kwargs)[source]

Initialize a new augmentation sequential instance.

Parameters:
  • *args (Union[_AugmentationBase, ImageSequential, Lambda]) – Sequence of kornia augmentations

  • data_keys (list[str]) – List of inputs to augment (e.g., [“image”, “mask”, “boxes”])

  • **kwargs (Any) – Keyword arguments passed to K.AugmentationSequential

New in version 0.5: The **kwargs parameter.

forward(batch)[source]

Perform augmentations and update data dict.

Parameters:

batch (dict[str, torch.Tensor]) – the input

Returns:

the augmented input

Return type:

dict[str, torch.Tensor]

class torchgeo.transforms.RandomGrayscale(weights, p=0.1, same_on_batch=False, keepdim=False)[source]

Bases: IntensityAugmentationBase2D

Apply random transformation to grayscale according to a probability p value.

There is no single agreed upon definition of grayscale for MSI. Some possibilities include:

  • Average of all bands: \(\frac{1}{C}\) where \(C\) is the number of spectral channels.

  • RGB-only bands: \([0.299, 0.587, 0.114]\) for the RGB channels, 0 for all other channels.

  • PCA: the first principal component across the spectral axis computed via PCA, minimizes redundant information.

The weight vector you provide will be automatically rescaled to sum to 1 in order to avoid changing the intensity of the image.

New in version 0.5.

__init__(weights, p=0.1, same_on_batch=False, keepdim=False)[source]

Initialize a new RandomGrayscale instance.

Parameters:
  • weights (Tensor) – Weights applied to each channel to compute a grayscale representation. Should be the same length as the number of channels.

  • p (float) – Probability of the image to be transformed to grayscale.

  • same_on_batch (bool) – Apply the same transformation across the batch.

  • keepdim (bool) – Whether to keep the output shape the same as input (True) or broadcast it to the batch form (False).

apply_transform(input, params, flags, transform=None)[source]

Apply the transform.

Parameters:
Returns:

The augmented input.

Return type:

Tensor

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