3band indices (#414)

* added triband normalized difference index to support indices e.g. Green-Blue NDVI

added triband normalized difference index base class to support indices e.g. Green-Blue NDVI

* Formatted

* formatted the comments

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* Update indices.py

* Update indices.py

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* frmtted

* formatted the init

* added test cases

* removed probelematic code

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tests/transforms/test_indices.py

@@ -18,6 +18,7 @@
     AppendNDWI,
     AppendNormalizedDifferenceIndex,
     AppendSWI,
+    AppendTriBandNormalizedDifferenceIndex,
 )
 
 
@@ -64,6 +65,13 @@ def test_append_index_batch(batch: Dict[str, Tensor]) -> None:
     assert output["image"].shape == (b, c + 1, h, w)
 
 
+def test_append_triband_index_batch(batch: Dict[str, Tensor]) -> None:
+    b, c, h, w = batch["image"].shape
+    tr = AppendTriBandNormalizedDifferenceIndex(index_a=0, index_b=0, index_c=0)
+    output = tr(batch)
+    assert output["image"].shape == (b, c + 1, h, w)
+
+
 @pytest.mark.parametrize(
     "index",
     [

torchgeo/transforms/__init__.py

@@ -14,6 +14,7 @@
     AppendNDWI,
     AppendNormalizedDifferenceIndex,
     AppendSWI,
+    AppendTriBandNormalizedDifferenceIndex,
 )
 from .transforms import AugmentationSequential
 
@@ -29,6 +30,7 @@
     "AppendNDWI",
     "AppendSWI",
     "AugmentationSequential",
+    "AppendTriBandNormalizedDifferenceIndex",
 )
 
 # https://stackoverflow.com/questions/40018681

torchgeo/transforms/indices.py

@@ -301,3 +301,66 @@ def __init__(self, index_nir: int, index_vre1: int) -> None:
             index_vre1: index of the Red Edge band, B5 in Sentinel 2 imagery
         """
         super().__init__(index_a=index_nir, index_b=index_vre1)
+
+
+class AppendTriBandNormalizedDifferenceIndex(Module):
+    r"""Append normalized difference index involving 3 bands as channel to image tensor.
+
+    Computes the following index:
+
+    .. math::
+
+       \text{NDI} = \frac{A - {B + C}}{A + {B + C}}
+
+    .. versionadded:: 0.3
+    """
+
+    def __init__(self, index_a: int, index_b: int, index_c: int) -> None:
+        """Initialize a new transform instance.
+
+        Args:
+            index_a: reference band channel index
+            index_b: difference band channel index of component 1
+            index_c: difference band channel index of component 2
+        """
+        super().__init__()
+        self.dim = -3
+        self.index_a = index_a
+        self.index_b = index_b
+        self.index_c = index_c
+
+    def _compute_index(self, band_a: Tensor, band_b: Tensor, band_c: Tensor) -> Tensor:
+        """Compute tri-band normalized difference index.
+
+        Args:
+            band_a: reference band tensor
+            band_b: difference band tensor component 1
+            band_c: difference band tensor component 2
+
+        Returns:
+            the index
+        """
+        return (band_a - (band_b + band_c)) / ((band_a + band_b + band_c) + _EPSILON)
+
+    def forward(self, sample: Dict[str, Tensor]) -> Dict[str, Tensor]:
+        """Compute and append tri-band normalized difference index to image.
+
+        Args:
+            sample: a sample or batch dict
+
+        Returns:
+            the transformed sample
+        """
+        if "image" in sample:
+            index = self._compute_index(
+                band_a=sample["image"][..., self.index_a, :, :],
+                band_b=sample["image"][..., self.index_b, :, :],
+                band_c=sample["image"][..., self.index_c, :, :],
+            )
+            index = index.unsqueeze(self.dim)
+
+            sample["image"] = torch.cat(  # type: ignore[attr-defined]
+                [sample["image"], index], dim=self.dim
+            )
+
+        return sample