Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model
Algorithm 2
Stacked autoencoder (SAE) 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 AE; the training algorithm is as in Algorithm 1
Step 4: use the hidden layer of the first layer of AE as the input layer of the second layer of AE and train layer by layer in turn until the last layer of AE
Step 5: connect the last output layer to the classifier to complete fine-tuning
