Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.

[1]  Simon X. Yang,et al.  Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network , 2018, Sensors.

[2]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[3]  Hong Li,et al.  Hyperspectral Image Classification Using Functional Data Analysis , 2014, IEEE Transactions on Cybernetics.

[4]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[5]  Tapani Raiko,et al.  Gaussian-Bernoulli deep Boltzmann machine , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[6]  Roland Doerffer,et al.  Rosis - An Imaging Spectrometer For Rejiote Sensing Of Chlorophyll Fluorescence , 1989, Photonics West - Lasers and Applications in Science and Engineering.

[7]  Zhou Guo,et al.  On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery , 2015 .

[8]  Yoshua Bengio,et al.  On the Expressive Power of Deep Architectures , 2011, ALT.

[9]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[10]  Mohammad Hossein Anisi,et al.  Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of Vehicles , 2018, Sensors.

[11]  Kai Xie,et al.  Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning , 2018, Sensors.

[12]  Shihong Du,et al.  Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Bin Wang,et al.  A Novel Spatial–Spectral Similarity Measure for Dimensionality Reduction and Classification of Hyperspectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Farid Melgani,et al.  Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[16]  Jon Atli Benediktsson,et al.  Advances in Spectral-Spatial Classification of Hyperspectral Images , 2013, Proceedings of the IEEE.

[17]  Mohammad Norouzi,et al.  Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Geoffrey E. Hinton,et al.  Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.

[19]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.