Abstract
The convolutional neural networks have achieved very good results in the field of remote sensing image classification and recognition. However, the cost of huge computational complexity with the significant accuracy improvement of CNNs makes a huge challenge to hardware implementation. A promising solution is FPGA due to it supports parallel computing with low power consumption. In this paper, LeNet-5-based remote sensing image classification method is implemented on FPGA. The test images with a size of 126 × 126 are transformed to the system from PC by serial port. The classification accuracy is 98.18% tested on the designed system, which is the same as that on PC. In the term of efficiency, the designed system runs 2.29 ms per image, which satisfies the real-time requirements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Garea AS, Heras DB, Argüello F. Caffe CNN-based classification of hyperspectral images on GPU. J Supercomputing. 2018;3:1–13.
Hu F, Xia GS, Hu J, et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 2015;7(11):14680–707.
Liu W, Ma L, Chen H. Arbitrary-oriented ship detection framework in optical remote-sensing images. IEEE Geosci Remote Sens Lett. 2018;99:1–5.
Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324.
Qiu J, Wang J, Yao S, et al. Going deeper with embedded FPGA platform for convolutional neural network. In: Acm/sigda international symposium on field-programmable gate arrays. ACM; 2016. p. 26–35.
Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. In: International conference on neural information processing systems. MIT Press; 2015. p. 91–9.
Vedaldi A, Lenc K. MatConvNet: convolutional neural networks for MATLAB; 2015:689–92.
Yang Y, Zhuang Y, Bi F, et al. M-FCN: effective fully convolutional network-based airplane detection framework. IEEE Geosci Remote Sens Lett. 2017;14(8):1293–7.
Zhang C, Li P, Sun G, et al. Optimizing FPGA-based accelerator design for deep convolutional neural networks. In: Acm/sigda international symposium on field-programmable gate arrays. ACM; 2015. p. 161–70.
Zhang C, Wu D, Sun J, et al. Energy-efficient CNN implementation on a deeply pipelined FPGA cluster. In: International symposium on low power electronics and design. ACM; 2016. p. 326–31.
Acknowledgements
This work was supported by the Chang Jiang Scholars Programmed under Grant T2012122 and the Youth Science and Technology Innovation Leader of National Innovation Talent Promotion Program under Grant No. 2013RA2034.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, L., Wei, X., Liu, W., Chen, H., Chen, L. (2020). Hardware Implementation of Convolutional Neural Network-Based Remote Sensing Image Classification Method. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_19
Download citation
DOI: https://doi.org/10.1007/978-981-13-6504-1_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6503-4
Online ISBN: 978-981-13-6504-1
eBook Packages: EngineeringEngineering (R0)