I have Landsat 8 preprocessed image I want to classify using random forest(RF) classification in python. The size of the image is 3,721,804 pixels with 7 bands. I have 250 training data shapefiles which were rasterized and yielded y (labels) and trainingData. The training labels(y) have five classes [1,2,3,4,5] with (250,) dimension. The trainingData has a dimension of (250,7) i.e. 250 sampling rasters each with 7 band.

I am encountering memoryerror when trying to predict the classes of the unknown (class)pixels because the process consumes so much RAM memory attributable to the byte-size of noSamples (rows x columns) in the input Landsat Image. I have read that Random Forest does not do well on sparse matrix (+1000 columns). But others have produced a classification using RF method.

How can I solve this problem?

The Code:

import numpy as np
import os
import pandas as pd
from osgeo import gdal,gdal_array,ogr
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier

from matplotlib import pyplot as plt

# Get the current working directory

# Open the dataset from the file
dataset = ogr.Open('./training/training_data.shp')
# Make sure the dataset exists -- it would be None if we couldn't open it
if not dataset:
    print('Error: could not open dataset')

#Let's get the driver from this file
driver = dataset.GetDriver()
print('Dataset driver is: {n}\n'.format(n=driver.name))

#How many layers are contained in this Shapefile?
layer_count = dataset.GetLayerCount()
print('The shapefile has {n} layer(s)\n'.format(n=layer_count))

#What is the name of the 1 layer?
layer = dataset.GetLayerByIndex(0)
print('The layer is named: {n}\n'.format(n=layer.GetName()))

# Get the spatial reference
spatial_ref = layer.GetSpatialRef()

# Export this spatial reference to something we can read... like the Proj4
proj4 = spatial_ref.ExportToProj4()
print('Layer projection is: {proj4}\n'.format(proj4=proj4))

### How many features are in the layer?
feature_count = layer.GetFeatureCount()
print('Layer has {n} features\n'.format(n=feature_count))

### How many fields are in the shapefile, and what are their names?
# First we need to capture the layer definition
defn = layer.GetLayerDefn()

# How many fields
field_count = defn.GetFieldCount()
print('Layer has {n} fields'.format(n=field_count))

# What are their names?
print('Their names are: ')
for i in range(field_count):
    field_defn = defn.GetFieldDefn(i)
    print('\t{name} - {datatype}'.format(name=field_defn.GetName(),

#In order to pair up our vector data with our input image raster pixels,
#we will need a way of co-aligning the datasets in space.
#First we will open our input raster image, to understand how we will want to rasterize our training vector
raster_ds = gdal.Open('./Landsat8_GeoTiff_Image/Sangamon_Image_Clip.tif', gdal.GA_ReadOnly)

# Fetch number of rows and columns
ncol = raster_ds.RasterXSize
nrow = raster_ds.RasterYSize

# Fetch projection and extent
proj = raster_ds.GetProjectionRef()
ext = raster_ds.GetGeoTransform()

raster_ds = None

# Create the raster dataset
memory_driver = gdal.GetDriverByName('GTiff')
out_raster_ds = memory_driver.Create('./training/training_data.gtif', ncol, nrow, 1, gdal.GDT_Byte)

# Set the ROI image(rasterized training vector)'s projection and extent to our input raster's projection and extent
# Fill our output band with the 0 blank, no class label, value
b = out_raster_ds.GetRasterBand(1)
# Rasterize the shapefile layer to our new dataset
status = gdal.RasterizeLayer(out_raster_ds,  # output to our new dataset
                             [1],  # output to our new dataset's first band
                             layer,  # rasterize this layer
                             None, None,  # don't worry about transformations since we're in same projection
                             [0],  # burn value 0
                             ['ALL_TOUCHED=TRUE',  # rasterize all pixels touched by polygons
                              'ATTRIBUTE=itree_New_']  # put raster values according to the 'itree_New_' field values

# Close dataset
out_raster_ds = None

# Check rasterized layer
roi_ds = gdal.Open('./training/training_data.gtif', gdal.GA_ReadOnly)

roi = roi_ds.GetRasterBand(1).ReadAsArray()

if status != 0:
    print("I don't think it worked...")

# How many pixels are in each class?
classes = np.unique(roi)
# Iterate over all class labels in the ROI image, printing out some information
for c in classes:
    print('Class {c} contains {n} pixels'.format(c=c,
                                                 n=(roi == c).sum()))

# Tell GDAL to throw Python exceptions, and register all drivers

#Read in our image and ROI image
img_ds = gdal.Open('./Landsat8_GeoTiff_Image/Sangamon_Image_Clip.tif', gdal.GA_ReadOnly)

roi_ds = gdal.Open('./training/training_data.gtif', gdal.GA_ReadOnly)

img = np.zeros((img_ds.RasterYSize, img_ds.RasterXSize, img_ds.RasterCount),
for b in range(img.shape[2]):
    img[:, :, b] = img_ds.GetRasterBand(b + 1).ReadAsArray()

roi = roi_ds.GetRasterBand(1).ReadAsArray().astype(np.uint8)

# In order to plot a multiband image , we need to normalize
# the array of each band and also, you should convert the data to float32 or uInt8 for matplotlib.

def normalize(img):
    ''' Function to normalize an input array to 0-1 '''
    img_min = img.min()
    img_max = img.max()
    return (img - img_min) / (img_max - img_min)

# Display them
#plt.imshow(img[:, :, 4], cmap=plt.cm.Greys_r)
plt.title('Normalized Multiband Image')

plt.title('ROI Training Data')

def createGeotiff(outRaster, data, geo_transform, projection):
    # Create a GeoTIFF file with the given data
    driver = gdal.GetDriverByName('GTiff')
    rows, cols = data.shape
    rasterDS = driver.Create(outRaster, cols, rows, 1, gdal.GDT_Byte)
    band = img_ds.GetRasterBand(1)
    dataset = None

'''Now, We have the ROI Image(rasterized training point shapefiles) and the input multiband image,
let's pair Y with X. The image we want to classify is our X feature inputs, 
and the ROI with the land cover labels is our Y labeled data,
 we need to pair them up in NumPy arrays so we may feed them to Random Forest:'''

# Find how many non-zero entries we have -- i.e. how many training data samples?
n_samples = (roi > 0).sum()
print('We have {n} samples'.format(n=n_samples))

# What are our classification labels?
labels = np.unique(roi[roi > 0])
print('The training data include {n} classes: {classes}'.format(n=labels.size,
# We will need a "X" matrix containing our features, and a "y" array containing our labels
#     These will have n_samples rows
#     In other languages we would need to allocate these and them loop to fill them, but NumPy can be faster

X = img[roi > 0, :]  # include 8th band, which is Fmask, for now
y = roi[roi > 0]
# The matrix is number of samples(250) x number of bands(7)
print('Our X matrix is sized: {sz}'.format(sz=X.shape))
print('Our y array is sized: {sz}'.format(sz=y.shape))

'''Training the Random Forest
Now that we have our X matrix of feature inputs (the spectral bands) and
our y array (the labels), we can train our model.'''

# Initialize our model with 500 trees
rf = RandomForestClassifier(n_estimators=1000, oob_score=True)

# Fit our model to training data
rf = rf.fit(X, y)

print('Our OOB("Out-Of-Bag") prediction of accuracy is: {oob}%'.format(oob=rf.oob_score_ * 100))

'''To help us get an idea of which spectral bands were important,
 we can look at the feature importance scores:'''

bands = [1, 2, 3, 4, 5, 7, 6]

for b, imp in zip(bands, rf.feature_importances_):
    print('Band {b} importance: {imp}'.format(b=b, imp=imp))

# Extract band's data and transform into a numpy array
bandsData = []
for b in range(1, img_ds.RasterCount+1):
    band = img_ds.GetRasterBand(b)
bandsData = np.dstack(bandsData)
rows, cols, noBands = bandsData.shape

# Prepare training data (set of pixels used for training) and labels
# The nonzero pixels are training data

print("Training Data dimension",X.shape)
print("Labels data dimension",y.shape)

# Train a Random Forest classifier
classifier = RandomForestClassifier(n_jobs=1, n_estimators=10)
classifier.fit(X, y)

# Predict class label of unknown pixels
noSamples = rows*cols
print ("The number of pixel Samples from the input image is:", noSamples,"with", noBands, "bands")
flat_pixels = bandsData.reshape((noSamples, noBands))
result = classifier.predict(flat_pixels)
classification = result.reshape((rows, cols))

# Create a GeoTIFF file with the given data
createGeotiff(outRaster, classification, geo_transform, projection)

img = Image.open('randomForest.tiff')

You are getting this error as your computer is unable to allocate enough memory to classify that many features at once.

The simplest way to solve the error would be to iterate through the pixels by block, and to classify these separately. This is easy to achieve with the numpy.array_split and numpy.concatenate methods. Note in your case it is best to use numpy.array_split rather than numpy.split as this allows the array to be split into uneven subsets.


flat_pixels = bandsData.reshape((noSamples, noBands))

flat_pixels_subsets = numpy.array_split(flat_pixels, 50)
# The second argument is the number of subsets. If it still doesn't fit
# into memory you may need to increase this number.
# The output is a list of arrays

results = []

for subset in flat_pixels_subsets:
    result_subset = classifier.predict(subset)

result = numpy.concatenate(results)
classification = result.reshape(cols, rows)

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