Calculate new band for satellite image after predict values with RF

Question

I have satellite image which contains 3 different bands. I'm using Python (jupyter notebook) in order to calculate new band by applying random forest regression. My problem is that after I have predict all the values for the new pixels, I don't know how to take it back to the original dataframe with the original bands in order to create in the end new image.

This is the process I did:

1. open the 3 bands image with rasterio, the band has this shape: (3, 869, 1202)

2. create pandas df when each row represents a pixel and each column is a band :

   

3. train the data and fit to random forest:

   #split the data
   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
   
   #import the algorithm
   rf=RandomForestRegressor()
   
   #reshape the y_train to fit the the model
   y_train=y_train.values.ravel()
   
   #fit the model
   rf.fit(X_train,y_train)
   
   rf_pred=rf.predict(X_test)
   

4. after checking the results, apply it to the full dataset and not only to training and set in order to predict the new band:

   #create the data
   data=df.iloc[:,1:]
   
   pred_all=rf.predict(data)
   
   #reshape to one column:
   pred_all.reshape(1006560,1)
   

   So after this, I don't know how to take this predicted values back to my table or to "link" it with the original pixel entities.

My end goal is to be able in the end to have this predicted values as new band so I can create image with the new predicted values.

Answer 1

The output will maintain the same order as it was predicted. You can use pd.concat to join it back to the original data on axis = 1.

# Re-run random forest using all the data we have available in our train set to predict accross the map area
random_forest_2 = RandomForestClassifier(n_estimators=1000, n_jobs = -1, oob_score = True)

random_forest_2.fit(Model_data_X, Model_data_Y)

#Run prediction on our apply dataset
print ('Performing prediction')
Model_apply = apply_zStats.drop('FOREST_ID', axis = 1)
Model_apply_predict = random_forest_2.predict(Model_apply)

#create dataframe
Model_apply_predict_df = pd.DataFrame(Model_apply_predict)

# Join predictions to FID and output
output = pd.DataFrame(apply_zStats['FID'])
output_merge = pd.concat([output.reset_index(drop=True), Model_apply_predict_df], axis=1)
output_merge.columns = ['FID','Class']

# Join back the training data
output_final = output_merge.append(reference_data, ignore_index = True)