Update README.md

README.md

@@ -66,10 +66,8 @@ The `opensr-test` package provides five datasets for benchmarking SR models. The
     - **`roi:`** The spatial unique identifier.
     - **`lr_gee_id:`** The low-resolution image Google Earth Engine id.
     - **`reflectance:`** How SR affects the mean of reflectance values. It uses the L1 norm. The lower the value, the better the reflectance consistency.
-    - **`spectral:`** How the harmonization affects the spectral signature, when compared to the LR image. It uses the spectral angle distance. The lower the value, the better the spectral consistency. The values are in degrees.
-    - **`spatial:`** The spatial misalignment in term of LR pixels (10m). The lower the value, the better the spatial consistency.
-- **`gradient_threshold`**: Threshold estimated by human interpretation. All the pixels with a gradient higher than this threshold are considered as features, and therefore, used to estimate the percentage of hallucination, omission, and correctness by the SR model.
-We provide 12 bands for Sentinel-2 L2A (see table below) and 13 for Sentinel-2 L1C (see table below).
+    - **`spectral:`** This shows how the harmonization affects the spectral signature compared to the LR image. It uses the spectral angle distance. The lower the value, the better the spectral consistency. The values are in degrees.
+    - **`spatial:`** The spatial misalignment in terms of LR pixels (10m). The lower the value, the better the spatial consistency.
 
 | Band | Description | Resolution (m) | L2A Index | L1C index |
 |------|-------------|----------------|-------| -------|
@@ -90,7 +88,7 @@ We provide 12 bands for Sentinel-2 L2A (see table below) and 13 for Sentinel-2 L
 
 ### **NAIP (X4 scale factor)**
 
-The National Agriculture Imagery Program (NAIP) dataset is a high-resolution aerial imagery dataset that covers the continental United States. The dataset consists of 2.5m NAIP imagery captured in the visible and near-infrared spectrum (RGBNIR) and all Sentinel-2 L1C and L2A bands. The dataset focus in **crop fields, forests, and bare soil areas**.
+The National Agriculture Imagery Program (NAIP) dataset is a high-resolution aerial imagery dataset covering the continental United States. It consists of 2.5m NAIP imagery captured in the visible and near-infrared spectrum (RGBNIR) and all Sentinel-2 L1C and L2A bands. The dataset focuses on crop fields, forests, and bare soil areas.
 
 ```python
 import opensr_test
@@ -104,7 +102,7 @@ naip = opensr_test.load("naip")
 
 ### **SPOT (X4 scale factor)**
 
-The SPOT imagery were obtained from the worldstat dataset. The dataset consists of 2.5m SPOT imagery captured in the visible and near-infrared spectrum (RGBNIR) and all Sentinel-2 L1C and L2A bands. The dataset focus in **urban areas, crop fields, and bare soil areas**.
+The SPOT imagery was obtained from the Worldstat dataset. The dataset consists of 2.5m SPOT imagery captured in the visible and near-infrared spectrum (RGBNIR) and all Sentinel-2 L1C and L2A bands. It focuses on urban areas, crop fields, and bare soil areas.
 
 ```python
 import opensr_test
@@ -119,7 +117,7 @@ spot = opensr_test.load("spot")
 
 ### **Venµs (X2 scale factor)**
 
-The Venµs images were obtained from the [**Sen2Venµs dataset**](https://zenodo.org/records/6514159). The dataset consists of 5m Venµs imagery captured in the visible and near-infrared spectrum (RGBNIR) and all Sentinel-2 L1C and L2A bands. The dataset focus in **crop fields, forests, urban areas, and bare soil areas**.
+The Venµs images were obtained from the [**Sen2Venµs dataset**](https://zenodo.org/records/6514159). The dataset consists of 5m Venµs imagery captured in the visible and near-infrared spectrum (RGBNIR) and all Sentinel-2 L1C and L2A bands. The dataset focuses on **crop fields, forests, urban areas, and bare soil areas**.
 
 ```python
 import opensr_test
@@ -133,7 +131,7 @@ venus = opensr_test.load("venus")
 
 ### **SPAIN CROPS (x4 scale factor)**
 
-The SPAIN CROPS dataset consists of 2.5m aerial imagery captured in the visible and near-infrared spectrum (RGBNIR) by the Spanish National Geographic Institute (IGN). The dataset includes all Sentinel-2 L1C and L2A bands. The dataset focus in **crop fields and forests**.
+The SPAIN CROPS dataset consists of 2.5m aerial imagery captured in the visible and near-infrared spectrum (RGBNIR) by the Spanish National Geographic Institute (IGN). The dataset includes all Sentinel-2 L1C and L2A bands. The dataset focuses on **crop fields and forests**.
 
 ```python
 import opensr_test
@@ -147,7 +145,7 @@ spain_crops = opensr_test.load("spain_crops")
 
 ### **SPAIN URBAN (x4 scale factor)**
 
-The SPAIN URBAN dataset consists of 2.5m aerial imagery captured in the visible and near-infrared spectrum (RGBNIR) by the Spanish National Geographic Institute (IGN). The dataset includes all Sentinel-2 L1C and L2A bands. The dataset focus in **urban areas and roads**.
+The SPAIN URBAN dataset consists of 2.5m aerial imagery captured in the visible and near-infrared spectrum (RGBNIR) by the Spanish National Geographic Institute (IGN). The dataset includes all Sentinel-2 L1C and L2A bands. The dataset focuses on **urban areas and roads**.
 
 ```python