torchgeo.transforms

TorchGeo transforms.

class torchgeo.transforms.AppendBNDVI(index_nir, index_blue)

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:

• https://doi.org/10.1016/S1672-6308(07)60027-4

New in version 0.3.

__init__(index_nir, index_blue)

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)

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:

• https://doi.org/10.1016/S1672-6308(07)60027-4

New in version 0.3.

__init__(index_nir, index_green, index_blue)

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)

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:

• https://doi.org/10.2134/agronj2001.933583x

New in version 0.3.

__init__(index_nir, index_green)

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)

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:

• https://doi.org/10.1016/S1672-6308(07)60027-4

New in version 0.3.

__init__(index_nir, index_green, index_red)

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)

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:

• https://www.yumpu.com/en/document/view/24226870/the-normalized-burn-ratio-and-relationships-to-burn-severity-/7

New in version 0.2.

__init__(index_nir, index_swir)

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)

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:

• https://doi.org/10.1080/01431160304987

__init__(index_swir, index_nir)

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)

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:

• https://agris.fao.org/agris-search/search.do?recordID=US201300795763

New in version 0.3.

__init__(index_nir, index_vre1)

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)

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:

• https://doi.org/10.1109/IGARSS.1994.399618

__init__(index_green, index_swir)

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)

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:

• https://doi.org/10.1016/0034-4257(79)90013-0

__init__(index_nir, index_red)

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)

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:

• https://doi.org/10.1080/01431169608948714

__init__(index_green, index_nir)

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)

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)

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)

Apply the transform.

Parameters:

• input (Tensor) – the input tensor

• params (dict[str, torch.Tensor]) – generated parameters

• flags (dict[str, int]) – static parameters

• transform (torch.Tensor | None) – the geometric transformation tensor

Returns:

the augmented input

Return type:

Tensor

class torchgeo.transforms.AppendRBNDVI(index_nir, index_red, index_blue)

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:

• https://doi.org/10.1016/S1672-6308(07)60027-4

New in version 0.3.

__init__(index_nir, index_red, index_blue)

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)

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:

• https://doi.org/10.3390/w13121647

New in version 0.3.

__init__(index_vre1, index_swir2)

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)

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)

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)

Apply the transform.

Parameters:

• input (Tensor) – the input tensor

• params (dict[str, torch.Tensor]) – generated parameters

• flags (dict[str, int]) – static parameters

• transform (torch.Tensor | None) – the geometric transformation tensor

Returns:

the augmented input

Return type:

Tensor

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

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)

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)

Apply the transform.

Parameters:

• input (Tensor) – The input tensor.

• params (dict[str, torch.Tensor]) – Generated parameters.

• flags (dict[str, torch.Tensor]) – Static parameters.

• transform (torch.Tensor | None) – The geometric transformation tensor.

Returns:

The augmented input.

Return type:

Tensor

class torchgeo.transforms.Rearrange(*args, **kwargs)

Bases: GeometricAugmentationBase3D

Rearrange tensor dimensions.

Examples

To insert a time dimension:

Rearrange('b (t c) h w -> b t c h w', c=1)

To collapse the time dimension:

Rearrange('b t c h w -> b (t c) h w')

__init__(*args, **kwargs)

Initialize a Rearrange instance.

Parameters:

• *args (Any) – Positional arguments for einops.rearrange().

• **kwargs (Any) – Keyword arguments for einops.rearrange().

apply_transform(input, params, flags, transform=None)

Apply the rearrangement to the input tensor.

Parameters:

• input (Tensor) – the input tensor

• params (dict[str, torch.Tensor]) – generated parameters

• flags (dict[str, Any]) – static parameters

• transform (torch.Tensor | None) – the geometric transformation tensor

Returns:

The rearranged tensor.

Return type:

Tensor

compute_transformation(input, params, flags)

Compute the transformation.

Parameters:

• input (Tensor) – the input tensor

• params (dict[str, torch.Tensor]) – generated parameters

• flags (dict[str, Any]) – static parameters

Returns:

the transformation

Return type:

Tensor

class torchgeo.transforms.SatSlideMix(gamma=1, beta=(0.0, 1.0), p=0.5)

Bases: GeometricAugmentationBase2D

Applies the Sat-SlideMix augmentation to a batch of images and masks.

Sat-SlideMix rolls (circularly shifts) images along either the height or width axis by a random amount.

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

• https://doi.org/10.1609/aaai.v39i27.35028

New in version 0.8.

__init__(gamma=1, beta=(0.0, 1.0), p=0.5)

Initialize a new SatSlideMix instance.

Parameters:

• gamma (int) – The number of augmented samples to create for each input image. The output batch size will be gamma * B.

• beta (torch.Tensor | float | tuple[float, float] | list[float]) – The range of percentage (0.0 to 1.0) of the image dimension (height or width) to shift.

• p (float) – Probability to apply the augmentation on each sample

Raises:

AssertionError – If gamma is not a positive integer.

generate_parameters(batch_shape)

Generate parameters for the batch.

compute_transformation(input, params, flags)

Compute the transformation.

Parameters:

• input (Tensor) – the input tensor

• params (dict[str, torch.Tensor]) – generated parameters

• flags (dict[str, Any]) – static parameters

Returns:

the transformation

Return type:

Tensor

apply_transform(input, params, flags, transform=None)

Apply the transform to the input image or mask.

Parameters:

• input (Tensor) – the input tensor image or mask

• params (dict[str, torch.Tensor]) – generated parameters

• flags (dict[str, Any]) – static parameters

• transform (torch.Tensor | None) – the geometric transformation tensor

Returns:

the augmented input

Return type:

Tensor