Abstract
High resolution remote sensing images can describe the geometric features, spatial features and texture features of objects more accurately, which are widely used in various fields. How to get more useful information from the remote sensing image, and then the recognition and classification of the image from the information has become one of the hot spots in the field of high resolution remote sensing image research. Deep learning is a learning algorithm based on the depth network structure, which can better fit the intrinsic structure of the sample, compared with the traditional shallow classifier. Depth of learning in a deep belief network model is based on single-layer Boltzmann machine learning algorithm, each layer is made up of the generation and cognition, and make the bidirectional weight updatin g come true, the net output of each layer can be reduced to the input signal, so that the model can be infinitely close to the global optimum in the pre training stage. The author propose an improved dropout strategy based on the study of deep belief network model, this strategy only chooses partial local area data to zero out the weight at each time. It not only maintains the local information of the image itself, but also enhances the generalization ability of the model. The experimental results show that the improved dropout strategy improves about 2.5% of the classification accuracy, and it has better classification performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Li, X., Wang, G.: Optimal band selection for hyper spectral data with improved differential evolution. J. Ambient Intell. Human. Comput. 6(5), 675–688 (2015)
Song, T., Pan, L.: Spiking neural P systems with request rules. Neurocomputing 193(12), 193–200 (2016)
Song T., Liu X., Zhao Y., Zhang X.: Spiking neural P systems with white hole neurons, IEEE Trans. Nanobiosci. (2016). doi:10.1109/TNB.2016.2598879
Li, X., Wang, L.: On the study of fusion techniques for bad geological remote sensing image. J. Ambient Intell. Human. Comput. 6(1), 994–1004 (2015)
Wang, Q.Q., Li, X., Wang, L.L.: Research and analysis method based on the classification on the bad geological identification. Geol. Sci. Technol. Inf. 33(6), 203–208 (2014)
Chen, G.Y., Li, X., An, K.: Identification and classification of adverse geological body based on convolution neural networks. Geol. Sci. Technol. Inf. 35(1), 205–211 (2016)
Chen, G.Y., Li, X., Wang, L.L.: Identification and classification of remote sensing image of vegetation based on big data. Geol. Sci. Technol. Inf. 35(3), 199–204 (2016)
Song, T., Pan, Z., Wong, D.M., Wang, X.: Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control. Inf. Sci. 372, 380–391 (2016)
Wang, X., Song, T., Gong, F., Pan, Z.: On the computational power of spiking neural P systems with self-organization. Sci. Rep. 6, 27624 (2016). doi:10.1038/srep27624
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Hinton, G.E.: A practical guide to training restricted Boltzmann machines. Momentum 9(1), 926 (2010)
Hinton, G.E., Srivastava, N., Krizhevsky, A.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Shi, X., Wu, X., Song, T., Li, X.: Construction of DNA nanotubes with controllable diameters and patterns by using hierarchical DNA sub-tiles. Nanoscale 8, 14785–14792 (2016). doi:10.1039/C6NR02695H
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, G., Li, X., Liu, L. (2016). A Study on the Recognition and Classification Method of High Resolution Remote Sensing Image Based on Deep Belief Network. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_29
Download citation
DOI: https://doi.org/10.1007/978-981-10-3611-8_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3610-1
Online ISBN: 978-981-10-3611-8
eBook Packages: Computer ScienceComputer Science (R0)