Image classification with Python

Image classification with Python


March 3, 2016

We are going to classify a multitemporal image stack of MODIS NDVI time series (MOD13Q1). The stack consists of 23 bands (16-day composites) with a spatial resolution of 231m in sinusoidal projection. We want to classify the different land use types, especially to discriminate different crop types.

Install Python and required image processing and scientific programming packages:

sudo apt-get install python-numpy scikit-learn scikit-image

For beginners, the basic Python IDLE is sufficient for scripting. However, another IDE, LiClipse is highly recommended:

We assume that you already have created a bunch of training samples in 8bit TIFF format with distinct class labeling (1,2,3,4, etc). It is necessary that these single pixels are snapped to the pixel size of the original dataset and have the same dimensions and extent.

Now write your Training script:

# import all required Python packages:
import as io
import numpy as np
import os, shutil
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier
from sklearn.externals import joblib

# set up your directories with the MODIS data
 rootdir = "C:\\Data\\Raster\\MODIS\\"
# path to your training data
path_pix = "C:\\Data\\Samples\\"
# path to your model
path_model = "C:\\Data\\Models\\"
# path to your classification results
path_class = "C:\\Data\\Class\\"

 # declare a new function
 def training():
    # path to your MODIS TIFF  
    raster = rootdir + "modis_stack_ndvi.tif"
    # path to your corresponding pixel samples (training data) 
    samples = path_pix + "samples_modis.tif"  

    # read in MODIS raster
    img_ds = io.imread(raster)
    # convert to 16bit numpy array 
    img = np.array(img_ds, dtype='int16')

    # do the same with your sample pixels 
    roi_ds = io.imread(samples)   
    roi = np.array(roi_ds, dtype='int8')  
    # read in your labels
    labels = np.unique(roi[roi > 0]) 
    print('The training data include {n} classes: {classes}'.format(n=labels.size, classes=labels))

    # compose your X,Y data (dataset - training data)     
    X = img[roi > 0, :] 
    Y = roi[roi > 0]     

    # assign class weights (class 1 has the weight 3, etc.)
    weights = {1:3, 2:2, 3:2, 4:2}

    # build your Random Forest Classifier 
    # for more information:

    rf = RandomForestClassifier(class_weight = weights, n_estimators = 100, criterion = 'gini', max_depth = 4, 
                                min_samples_split = 2, min_samples_leaf = 1, max_features = 'auto', 
                                bootstrap = True, oob_score = True, n_jobs = 1, random_state = None, verbose = True)  

    # alternatively you may try out a Gradient Boosting Classifier 
    # It is much less RAM consuming and considers weak training data      
    rf = GradientBoostingClassifier(n_estimators = 300, min_samples_leaf = 1, min_samples_split = 4, max_depth = 4,    
                                    max_features = 'auto', learning_rate = 0.8, subsample = 1, random_state = None,         
                                    warm_start = True)

    # now fit your training data with the original dataset
    rf =,Y)

    # export your Random Forest / Gradient Boosting Model     
    model = path_model + "model.pkl"
    joblib.dump(rf, model)


And now write your classification script:

def classification():
    # Read worldfile of original dataset
    tfw_old = str(raster.split(".tif")[0]) + ".tfw"     

    # Read Data    
    img_ds = io.imread(raster)   
    img = np.array(img_ds, dtype='int16')    

    # call your random forest model
    rf = path_model + "model.pkl"          
    clf = joblib.load(rf)    

    # Classification of array and save as image (23 refers to the number of multitemporal NDVI bands in the stack) 
    new_shape = (img.shape[0] * img.shape[1], img.shape[2]) 
    img_as_array = img[:, :, :23].reshape(new_shape)   

    class_prediction = clf.predict(img_as_array) 
    class_prediction = class_prediction.reshape(img[:, :, 0].shape)  

    # now export your classificaiton
    classification = path_class  + "classification.tif" 
    io.imsave(classification, class_prediction)    

    # Assign Worldfile to classified image    
    tfw_new = classification.split(".tif")[0] + ".tfw"   
    shutil.copy(tfw_old, tfw_new)


Multitemporal NDVI MODIS Stack with clipped AOI:


Training pixels with distinct classes are created from field sampling points and snapped to the original raster extent:


Classification results (left image: same extent as above):

3 4




you may also like:

New guest researcher from Senegal: Ousmane Badji

New guest researcher from Senegal: Ousmane Badji

We are delighted to welcome Ousmane BADJI, our newest guest scientist, to Würzburg. Since his arrival in January 2024, he has integrated seamlessly into our research community. Ousmane has a multidisciplinary background in Agroforestry and Ecology, encompassing both...

semester wrap-up by our latest EAGLE generation

semester wrap-up by our latest EAGLE generation

Our latest generation of EAGLEs just wrote a nice blog post summarizing their first months within the EAGLE program, outlining how they bonded and formed a group plus how they experienced their first earth observation courses within our international M.Sc. program:...

Lecture at the DLR site colloquium in Oberpfaffenhofen

Lecture at the DLR site colloquium in Oberpfaffenhofen

"Into space for the earth" was the title of a lecture by Prof. Hannes Taubenböck that he gave on February 19th at the DLR site colloquium in Oberpfaffenhofen. The focus was on the question of how we can use remote sensing data, methods and results to help stimulate...