Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model
Algorithm 4
DBN training algorithm
Step 1: start
Step 2: given the parameters in Algorithm 1 and the number of hidden layer layers
Step 3: train the first layer of RBM and the training algorithm as in Algorithm 1
Step 4: use the hidden layer of the first RBM layer as the input layer of the second RBM layer and train layer by layer in turn until the last RBM layer
Step 5: connect the final layer of output to the classifier to complete fine-tuning
