normalize data from EO Learn API

eo_learn/py/s7RewriteTIF.py

@@ -28,14 +28,17 @@ def rewriteTIF(eopatch, file_name, vv_path, vh_path, dem_path):
     with rio.open(dem_path, 'r') as dst:
         dem = dst.read()
         
+    #Scale to range 0-1    
+    vv_data /= np.amax(vv_data)
+    vh_data /= np.amax(vh_data)
     
-    # Calculate VV/VH ratio from VV and VH data
-    #VV/VH ratio is calculated as VV/VH, since our data is measured on a linear scale
-    vvvh_ratio = np.empty(vv_data.shape, dtype=rio.float64)
-    vvvh_ratio = np.divide(vv_data, vh_data, out=np.zeros_like(vv_data), where = vh_data!=0)
-    
+    #Calculate an index that highlights water bodies as third band:
+    vvvh_sum = np.add(vv_data,vh_data)
+    vvvh_dif = np.subtract(vv_data,vh_data)
+    vvvh_index = np.divide(vvvh_dif,vvvh_sum,out=np.zeros_like(vvvh_sum), where=vvvh_sum!=0)
+
     # Zip VV, VH, and VV/VH arrays together into one array
-    stack = [[x]+[y]+[z] for x, y, z in zip(vv_data, vh_data, vvvh_ratio)]
+    stack = [[x]+[y]+[z] for x, y, z in zip(vv_data, vh_data, vvvh_index)]
     
     names = []
     # Write arrays to tif based on their data
@@ -47,7 +50,7 @@ def rewriteTIF(eopatch, file_name, vv_path, vh_path, dem_path):
             dst.write_band(2, stack[i][1])
             dst.write_band(3, stack[i][2])
             dst.write_band(4, dem[0])
-            dst.descriptions = tuple(['VV','VH','VV/VH_ratio','DEM'])
+            dst.descriptions = tuple(['VV','VH','VV/VH_index','DEM'])
             names.append(stack_filename)
     
     # Remove temporary VV and VH files

eo_learn/py/s8Visualise.py

@@ -7,40 +7,41 @@ import matplotlib.pyplot as plt
 import os
 
 def visualiseResults(visualise, bands, names, file_name):
-    for i in range(len(bands)):
-        if bands[i] == 'VV':
-            band = 0
-        elif bands[i] == 'VH':
-            band = 1
-        elif bands[i] == 'VVVH':
-            band = 2
-        elif bands[i] == 'DEM':
-            band = 3
-        
-        # Plot all images for each band in bands, expect for DEM (only plot DEM once)
-        if band != 3:
-            for x in range(len(names)):
-                path_stacked = os.path.join('data', file_name, names[x])
+    if visualise == 'y':
+        for i in range(len(bands)):
+            if bands[i] == 'VV':
+                band = 0
+            elif bands[i] == 'VH':
+                band = 1
+            elif bands[i] == 'VVVH':
+                band = 2
+            elif bands[i] == 'DEM':
+                band = 3
+            
+            # Plot all images for each band in bands, expect for DEM (only plot DEM once)
+            if band != 3:
+                for x in range(len(names)):
+                    path_stacked = os.path.join('data', file_name, names[x])
+                    SAR = rio.open(path_stacked)
+                    SARi = SAR.read(band+1)
+            
+                    date = names[x][0:10]
+                    fig, ax = plt.subplots(figsize=(10,10))
+                    rio.plot.show(SARi, ax=ax)
+                    plt.title(f'{bands[i]} {date}', fontdict={'fontsize': 20})
+                    plt.axis(False)
+                    
+                    del SAR
+            else:
+                path_stacked = os.path.join('data', file_name, names[0])
                 SAR = rio.open(path_stacked)
                 SARi = SAR.read(band+1)
         
-                date = names[x][0:10]
+                date = names[0][0:10]
                 fig, ax = plt.subplots(figsize=(10,10))
                 rio.plot.show(SARi, ax=ax)
                 plt.title(f'{bands[i]} {date}', fontdict={'fontsize': 20})
                 plt.axis(False)
                 
                 del SAR
-        else:
-            path_stacked = os.path.join('data', file_name, names[0])
-            SAR = rio.open(path_stacked)
-            SARi = SAR.read(band+1)
-    
-            date = names[0][0:10]
-            fig, ax = plt.subplots(figsize=(10,10))
-            rio.plot.show(SARi, ax=ax)
-            plt.title(f'{bands[i]} {date}', fontdict={'fontsize': 20})
-            plt.axis(False)
-            
-            del SAR