Abstract
Reducing the dimension of the hyperspectral image data can directly reduce the redundancy of the data, thus improving the accuracy of hyperspectral image classification. In this paper, the deep belief network algorithm in the theory of deep learning is introduced to extract the in-depth features of the imaging spectral image data. Firstly, the original data is mapped to feature space by unsupervised learning methods through the Restricted Boltzmann Machine (RBM). Then, a deep belief network will be formed by superimposed multiple Restricted Boltzmann Machines and training the model parameters by using the greedy algorithm layer by layer. At the same time, as the objective of data dimensionality reduction is achieved, the underground feature construction of the original data will be formed. The final step is to connect the depth features of the output to the Softmax regression classifier to complete the fine-tuning (FT) of the model and the final classification. Experiments using imaging spectral data showing the in-depth features extracted by the profound belief network algorithm have better robustness and separability. It can significantly improve the classification accuracy and has a good application prospect in hyperspectral image information extraction.
Highlights
Hyperspectral image classification is one of the most advanced techniques to understand the remote sensing image scene [1]
In this paper, based on the study of the dimensionality reduction method of traditional imaging spectral data, we introduce deep belief network based on the theory of deep learning to using a dimensionality reduction of hyperspectral images. e conventional dimension reduction method and deep belief network are compared to extract hyperspectral image information, and the robustness and separability of abstract features are considered
ROSIS-3 has been preprocessed. e Hyspex uses a radiation correction of the original image obtained by the imaging calibration spectrometer. e reflectance inversion was performed by the Flat Field (FF) method based on the statistical model, and the large cement floor was selected as the Flat Field
Summary
Hyperspectral image classification is one of the most advanced techniques to understand the remote sensing image scene [1]. In terms of increasing the data dimension and selecting more data samples, band information is extended to increase the redundancy of the model This improves the spectral resolution of hyperspectral remote sensing images, it dramatically affects the processing speed of the model data and reduces the accuracy of the model and affects the target recognition. In 2006, Hinton and Salakhutdino proposed using a deep belief network (DBN) [16] to achieve data dimensionality reduction and classification It is essentially the feature extraction of data using deep neural networks, called the deep learning algorithm. E conventional dimension reduction method and deep belief network are compared to extract hyperspectral image information, and the robustness and separability of abstract features are considered.
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