rslearn.train.transforms.sentinel1

sentinel1

Transforms related to Sentinel-1 data.

Sentinel1ToDecibels

Bases: Transform

Convert Sentinel-1 data from raw intensity to or from decibels.

Source code in rslearn/train/transforms/sentinel1.py

class Sentinel1ToDecibels(Transform):
    """Convert Sentinel-1 data from raw intensity to or from decibels."""

    def __init__(
        self,
        selectors: list[str] = ["image"],
        from_decibels: bool = False,
        epsilon: float = 1e-6,
        skip_missing: bool = False,
    ):
        """Initialize a new Sentinel1ToDecibels.

        Args:
            selectors: the input selectors to apply the transform on.
            from_decibels: convert from decibels to intensities instead of intensity to
                decibels.
            epsilon: when converting to decibels, clip the intensities to this minimum
                value to avoid log issues. This is mostly to avoid pixels that have no
                data with no data value being 0.
            skip_missing: if True, skip selectors that don't exist in the input/target
                dicts. Useful when working with optional inputs.
        """
        super().__init__(skip_missing=skip_missing)
        self.selectors = selectors
        self.from_decibels = from_decibels
        self.epsilon = epsilon

    def apply_image(self, image: RasterImage) -> RasterImage:
        """Normalize the specified image.

        Args:
            image: the image to transform.
        """
        if self.from_decibels:
            # Decibels to linear scale.
            image.image = torch.pow(10.0, image.image / 10.0)
        else:
            # Linear scale to decibels.
            image.image = 10 * torch.log10(torch.clamp(image.image, min=self.epsilon))
        return image

    def forward(
        self, input_dict: dict[str, Any], target_dict: dict[str, Any]
    ) -> tuple[dict[str, Any], dict[str, Any]]:
        """Apply normalization over the inputs and targets.

        Args:
            input_dict: the input
            target_dict: the target

        Returns:
            normalized (input_dicts, target_dicts) tuple
        """
        self.apply_fn(self.apply_image, input_dict, target_dict, self.selectors)
        return input_dict, target_dict

apply_image

apply_image(image: RasterImage) -> RasterImage

Normalize the specified image.

Parameters:

Name	Type	Description	Default
image	RasterImage	the image to transform.	required

Source code in rslearn/train/transforms/sentinel1.py

def apply_image(self, image: RasterImage) -> RasterImage:
    """Normalize the specified image.

    Args:
        image: the image to transform.
    """
    if self.from_decibels:
        # Decibels to linear scale.
        image.image = torch.pow(10.0, image.image / 10.0)
    else:
        # Linear scale to decibels.
        image.image = 10 * torch.log10(torch.clamp(image.image, min=self.epsilon))
    return image

forward

forward(input_dict: dict[str, Any], target_dict: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]

Apply normalization over the inputs and targets.

Parameters:

Name	Type	Description	Default
input_dict	dict[str, Any]	the input	required
target_dict	dict[str, Any]	the target	required

Returns:

Type	Description
tuple[dict[str, Any], dict[str, Any]]	normalized (input_dicts, target_dicts) tuple

Source code in rslearn/train/transforms/sentinel1.py

def forward(
    self, input_dict: dict[str, Any], target_dict: dict[str, Any]
) -> tuple[dict[str, Any], dict[str, Any]]:
    """Apply normalization over the inputs and targets.

    Args:
        input_dict: the input
        target_dict: the target

    Returns:
        normalized (input_dicts, target_dicts) tuple
    """
    self.apply_fn(self.apply_image, input_dict, target_dict, self.selectors)
    return input_dict, target_dict