I am working on LULC classification of Sentinel-2 image(4-bands) using Machine Learning algorithms. I split the whole image into 64*64 patches and assigned class label (builtup, barren land, water and vegetation) to selected patches. For each class patch spectral features (mean, variance values of each band, mean normalized difference vegetation index(NDVI) for each class patch, texture features (grey-level co-occurrence matrix (GLCM) contrast, homogeneity, dissimilarity and correlation for NIR band of each class patch were calculated. I split the features into training and testing dataset and trained random forest classifier on training dataset. The problem is I don't know how to classify the whole image using trained classifier and visualize it as a classified image.
import numpy as np import pandas as pd from sklearn.metrics import accuracy_score from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import cross_val_score from sklearn.metrics import confusion_matrix train = pd.read_excel(r'D:\band_composite\traning_data\training.xls')#import traing dataset test=pd.read_excel(r"D:\band_composite\test_data\test.xls")#import testing daatset # (Xtrain,Ytrain for training dataset) X_train = train.drop(['class'], axis=1) y_train = pd.DataFrame(train['class'].values) X_test = test.drop(['class'], axis=1) y_test = test['class'] # Random forest model training clf = RandomForestClassifier(max_depth=2, random_state=0) model=clf.fit(X_train, y_train) # X_train shape (141, 15) # y_train.shape (141, 1) #X_training dataset excel image