Reducing the dimension of the hyperspectral image data can directly reduce the redundancy of the data, thus improving the\naccuracy of hyperspectral image classification. In this paper, the deep belief network algorithm in the theory of deep learning is\nintroduced to extract the in-depth features of the imaging spectral image data. Firstly, the original data is mapped to feature space\nby unsupervised learning methods through the Restricted Boltzmann Machine (RBM). Then, a deep belief network will be formed\nby superimposed multiple Restricted Boltzmann Machines and training the model parameters by using the greedy algorithm layer\nby layer. At the same time, as the objective of data dimensionality reduction is achieved, the underground feature construction of\nthe original data will be formed. The final step is to connect the depth features of the output to the Softmax regression classifier to\ncomplete the fine-tuning (FT) of the model and the final classification. Experiments using imaging spectral data showing the indepth\nfeatures extracted by the profound belief network algorithm have better robustness and separability. It can significantly\nimprove the classification accuracy and has a good application prospect in hyperspectral image information extraction.
Loading....