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Blog: Land Use Classification using Convolutional Neural Networks

Land Use Type From UCMerced Dataset

UCMerced Land Use Image Dataset consists of 21 land use class like Forest, Runaway, Freeway, Harbour and others. In this tutorials, we would like to share how to create Simple Convolutional Neural Networks program to classify Land Use. Before continue please kindly prepare software as follow:
– Keras Python Library

After collecting some libraries, lets begin to create the code with 6 step-by-step as follow:

  1. Import Library requirement like Numpy, OpenCV, Matplotlib
import numpy as np
import cv2
import os
import re
import glob
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
from keras.optimizers import SGD
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from keras.models import load_model

2. Read Land Use Images

path = os.path.abspath('[YOUR_FILE_NAME]')
path = re.sub('[a-zA-Z\s._]+$', '', path)
dirs = os.listdir(path+'[YOUR_LAND_USE_PATH]')
label = 0
im_arr = []
lb_arr = []
X = []
y = []
for i in dirs:
count = 0
for pic in glob.glob(path+'[YOUR_LAND_USE_PATH]'+i+'/*.tif'):
im = cv2.imread(pic)
im = cv2.resize(im,(70,70))
im = np.array(im)
count = count + 1
if(count == 3):
print("size "+str(i)+" : "+str(count))
label = label + 1
X = np.array(X)
y = np.array(y);

3. Image Pre-Processing

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
X_train = X_train.astype('float32') #set x_train data type as float32
X_test = X_test.astype('float32') #set x_test data type as float32
X_train /= 255 #change x_train value between 0 - 1
X_test /= 255 #change x_test value between 0 - 1
y_train = keras.utils.to_categorical(y_train, 21) #change label to binary / categorical: [1 0 0 0] = 0, [0 1 0 0] = 1, so on
y_test = keras.utils.to_categorical(y_test, 21) #change label to binary / categorical

4. Training Data

model = Sequential() #model = sequential 
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=(70,70,3)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(Flatten()) #make layer flatten
model.add(Dense(128, activation='relu'))
model.add(Dense(21, activation='softmax'))
epochs = 25
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=32)
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))

After Training 10 Epoch, accuracy of data reach 55.8% that good enough for classify 21 class.

Source: Artificial Intelligence on Medium

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