Research on Hyperspectral Remote Sensing Image Classification Based on Deep Neural Networks
Classification is the most important part of hyperspectral remote sensing image processing.With view to the setbacks of complex preprocessing, difficult extraction of high-dimensional features and not accurate classification existing in the current classification method, the paper raises an algorithm of hyperspectral remote sensing image classification based on deep neural networks.This algorithm first uses the maximum noise fraction to reduce the feature space dimension, then combines the auto-encoders and the multinomial logistic regression classifier sofemax into a neural networks with multiple hidden layers, to extract and classify the unsupervised deep features of hyperspectral remote sensing images.Experiments show that, in comparison with the traditional linear support vector machine (SVM) classification method, it can extract more advanced expression features and realize better image classification accuracy in a shorter processing time.
NETL
NSTL
万方数据
维普
