Classification of Very High Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks

Algorithm 1

The pseudocode of the proposed CNN developed for land cover mapping using aerial images.

Algorithm 1: CNN for orthophoto classification

Input: RGB image () captured by the aerial remote sensing system, training/testing samples

()

Output: Land cover classification map with seven classes ()

I, D, O

Preprocessing (Section 3.1.2):

calibrate using the available 34 GCPs

normalize pixel values using Eq. 1

Classification (CNN) (Section 3.2.2 and Section 3.2.3):

for Patch_x_axis:

 initialize sum = 0

 for Patch_y_axis:

  calculate dot product(Patch, Filter)

 result_convolution (x, y) = Dot product

for Patch_x_axis:

 for Patch_y_axis:

  calculate Max (Patch)

result_maxpool (x, y) = Dot product

update F = max(0, x)

result_cnn_model = trained model

Prediction:

apply the trained model to the whole image and get

Mapping:

get the results of prediction

reshape the predicted values to the original image shape

convert the array to image and write it on the hard disk