Abstract
Real-time hyperspectral image classification is a necessary primitive in many remotely sensed image analysis applications. Previous work has shown that Support Vector Machines (SVMs) can achieve high classification accuracy, but unfortunately it is very computationally expensive. This paper presents a scalable dataflow accelerator on FPGA for real-time SVM classification of hyperspectral images.To address data dependencies, we adapt multi-class classifier based on Hamming distance. The architecture is scalable to high problem dimensionality and available hardware resources. Implementation results show that the FPGA design achieves speedups of 26x, 1335x, 66x and 14x compared with implementations on ZYNQ, ARM, DSP and Xeon processors. Moreover, one to two orders of magnitude reduction in power consumption is achieved for the AVRIS hyperspectral image datasets.
This work was partially supported by the Fundamental Research Funds for the Central Universities (Grant No. HIT.NSRIF.201615) and China Scholarship Council.
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Notes
- 1.
The source code can be found from http://www.esat.kuleuven.be/sista/lssvmlab/.
References
Bioucas-Dias, J.M., et al.: Hyperspectral remote sensing data analysis and future challenges. IEEE Geosci. Remote Sens. Mag. 6, 6–36 (2013)
Cadambi, S., Igor, D., et al.: A massively parallel FPGA-based coprocessor for support vector machines. In: Proceedings - IEEE Symposium on Field Programmable Custom Computing Machines, FCCM 2009, pp. 115–122 (2009)
Gustavo, C., Davis, T., et al.: Advances in hyperspectral image classification: earth monitoring with statistical learning methods. IEEE Sig. Process. Mag. 31(1), 45–54 (2014)
Irick, K.M., et al.: A hardware efficient support vector machine architecture for FPGA. In: Proceedings of the 16th IEEE Symposium on Field-Programmable Custom Computing Machines, FCCM 2008, pp. 304–305 (2008)
Khodadadzadeh, M., et al.: A new framework for hyperspectral image classification using multiple spectral and spatial features. In: IEEE Geoscience and Remote Sensing Symposium, pp. 4628–4631 (2014)
Kyrkou, C., Theocharides, T.: SCoPE: towards a systolic array for SVM object detection. IEEE Embed. Syst. Lett. 1(2), 46–49 (2009)
Liu, Y., et al.: Hyperspectral classification via deep networks and superpixel segmentation. Int. J. Remote Sens. 36(13), 3459–3482 (2015)
Lopez, S., et al.: The promise of reconfigurable computing for hyperspectral imaging onboard systems: a review and trends. Proc. IEEE 101(3), 698–722 (2013)
Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004)
Montenegro, S., et al.: Hyperspectral monitoring data processing, pp. 1–4 (2003). ISBN 3-89685-569-7
Papadonikolakis, M., Bouganis, C.S.: Novel cascade FPGA accelerator for support vector machines classification. IEEE Trans. Neural Netw. Learn. Syst. 23(7), 1040–1052 (2012)
Papadonikolakis, M., Bouganis, C.S.: A heterogeneous FPGA architecture for support vector machine training. In: 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), pp. 6–9 (2010)
Sami, Q., et al.: Neural network based adaboosting approach for hyperspectral data classification. In: International Conference on Computer Science and Network Technolgoy, pp. 241–245 (2011)
Christos, K., et al.: Embedded hardware-efficient real-time classification with cascade support vector machines. IEEE Trans. Neural Netw. Learn. Syst. 26(1), 99–112 (2016)
Xue, Z., et al.: Harmonic analysis for hyperspectral image classification integrated with PSO optimized SVM. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(6), 2131–2146 (2014)
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Wang, S., Niu, X., Ma, N., Luk, W., Leong, P., Peng, Y. (2016). A Scalable Dataflow Accelerator for Real Time Onboard Hyperspectral Image Classification. In: Bonato, V., Bouganis, C., Gorgon, M. (eds) Applied Reconfigurable Computing. ARC 2016. Lecture Notes in Computer Science(), vol 9625. Springer, Cham. https://doi.org/10.1007/978-3-319-30481-6_9
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