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The Journal of Supercomputing

Erschienen in:

04.02.2020

Scalable recurrent neural network for hyperspectral image classification

verfasst von: Mercedes E. Paoletti, Juan M. Haut, Javier Plaza, Antonio Plaza

Erschienen in: The Journal of Supercomputing | Ausgabe 11/2020

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Abstract

Hyperspectral imaging (HSI) collects hundreds of images over large spatial observation areas on the Earth’s surface, recording scenes at different wavelength channels and providing a vast amount of information. Recurrent neural networks (RNNs) have been widely used for the classification of HSI datasets, understood as a single sequence of pixel vectors with high dimensionality. However, the RNN model scales poorly when dealing with HSI scenes with large dimensionality. In order to mitigate this problem, this paper presents a new RNN classifier based on simple recurrent units that performs HSI classification in a highly scalable and efficient way. Our experimental results (conducted on four real HSI datasets) reveal very good performance, not only in terms of classification accuracy (in line with existing methods), but also in terms of computational performance when dealing with large datasets.

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Appleyard J, Kocisky T, Blunsom P (2016) Optimizing performance of recurrent neural networks on gpus. arXiv:1604.01946

Campos V, Jou B, Giró-i Nieto X, Torres J, Chang SF (2017) Skip rnn: learning to skip state updates in recurrent neural networks. arXiv:1708.06834

Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014) Deep learning-based classification of hyperspectral data. IEEE J Select Top Appl Earth Obs Remote Sens 7(6):2094–2107CrossRef

Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder-decoder approaches. arXiv:1409.1259

Cudahy T, Hewson R, Huntington J, Quigley M, Barry P (2001) The performance of the satellite-borne hyperion hyperspectral VNIR-SWIR imaging system for mineral mapping at Mount Fitton, South Australia. In: IGARSS 2001. Scanning the present and resolving the future. Proceedings. IEEE 2001 international geoscience and remote sensing symposium (Cat. No. 01CH37217). IEEE, vol 1, pp 314–316

Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303–314MathSciNetCrossRef

Gevaert CM, Suomalainen J, Tang J, Kooistra L (2015) Generation of spectral-temporal response surfaces by combining multispectral satellite and hyperspectral UAV imagery for precision agriculture applications. IEEE J Select Top Appl Earth Obs Remote Sens 8(6):3140–3146CrossRef

Goetz AF, Vane G, Solomon JE, Rock BN (1985) Imaging spectrometry for earth remote sensing. Science 228(4704):1147–1153CrossRef

Green RO, Eastwood ML, Sarture CM, Chrien TG, Aronsson M, Chippendale BJ, Faust JA, Pavri BE, Chovit CJ, Solis M, Olah MR, Williams O (1998) Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens Environ 65(3):227–248CrossRef

10.

Hochreiter S (1998) Recurrent neural net learning and vanishing gradient. Int J Uncertain Fuzziness Knowl-Based Syst 6(2):107–116MathSciNetCrossRef

11.

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRef

12.

Kumar DN, Reshmidevi T (2013) Remote sensing applications in water resources. J Indian Inst Sci 93(2):163–188

13.

Kunkel B, Blechinger F, Lutz R, Doerffer R, van der Piepen H, Schroder M (1988) ROSIS (Reflective Optics System Imaging Spectrometer)—A candidate instrument for polar platform missions. In: Seeley J, Bowyer S (eds) Proceedings of SPIE 0868 optoelectronic technologies for remote sensing from space, p 8 . https://doi.org/10.1117/12.943611

14.

Landgrebe D (2002) Hyperspectral image data analysis. IEEE Signal Process Mag 19(1):17–28CrossRef

15.

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436CrossRef

16.

Lei T, Zhang Y, Wang SI, Dai H, Artzi Y (2018) Simple recurrent units for highly parallelizable recurrence. In: Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, October 31–November 4, 2018, pp 4470–4481. https://doi.org/10.18653/v1/d18-1477

17.

Li B, Zhou E, Huang B, Duan J, Wang Y, Xu N, Zhang J, Yang H (2014) Large scale recurrent neural network on gpu. In: 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 4062–4069

18.

Li Z, Chen J, Baltsavias E (2008) Advances in photogrammetry, remote sensing and spatial information sciences: 2008 ISPRS congress book, vol 7. CRC Press, Boca RatonCrossRef

19.

Manolakis D, Shaw G (2002) Detection algorithms for hyperspectral imaging applications. IEEE Signal Process Mag 19(1):29–43CrossRef

20.

Meerdink SK, Roberts DA, Roth KL, King JY, Gader PD, Koltunov A (2019) Classifying California plant species temporally using airborne hyperspectral imagery. Remote Sens Environ 232:111308CrossRef

21.

Mou L, Ghamisi P, Zhu XX (2017) Deep recurrent neural networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(7):3639–3655CrossRef

22.

Murphy RJ, Monteiro ST, Schneider S (2012) Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors. IEEE Trans Geosci Remote Sens 50(8):3066–3080CrossRef

23.

Nurvitadhi E, Sim J, Sheffield D, Mishra A, Krishnan S, Marr D (2016) Accelerating recurrent neural networks in analytics servers: comparison of FPGA, CPU, GPU, and ASIC. In: 2016 26th International Conference on Field Programmable Logic and Applications (FPL). IEEE, pp 1–4

24.

Paoletti ME, Haut JM, Plaza J, Plaza A (2019) Deep learning classifiers for hyperspectral imaging: a review. ISPRS J Photogramm Remote Sens 158:279–317. https://doi.org/10.1016/j.isprsjprs.2019.09.006CrossRef

25.

Plaza A, Du Q, Chang YL, King RL (2011) High performance computing for hyperspectral remote sensing. IEEE J Select Top Appl Earth Obs Remote Sens 4(3):528–544CrossRef

26.

Srivastava RK, Greff K, Schmidhuber J (2015) Training very deep networks. In: Advances in neural information processing systems, pp 2377–2385

27.

Weber C, Aguejdad R, Briottet X, Avala J, Fabre S, Demuynck J, Zenou E, Deville Y, Karoui MS, Benhalouche FZ et al (2018) Hyperspectral imagery for environmental urban planning. In: IGARSS 2018-2018 IEEE international geoscience and remote sensing symposium. IEEE, pp 1628–1631

28.

Weninger F, Bergmann J, Schuller B (2015) Introducing currennt: the Munich open-source CUDA recurrent neural network toolkit. J Mach Learn Res 16(1):547–551MathSciNetMATH

29.

Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1(2):270–280CrossRef

30.

Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52CrossRef

31.

Xu Y, Zhang L, Du B, Zhang F (2018) Spectral–spatial unified networks for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(10):5893–5909

32.

Yang X, Ye Y, Li X, Lau RY, Zhang X, Huang X (2018) Hyperspectral image classification with deep learning models. IEEE Trans Geosci Remote Sens 56(9):5408–5423CrossRef

33.

Zhang X, Sun Y, Jiang K, Li C, Jiao L, Zhou H (2018) Spatial sequential recurrent neural network for hyperspectral image classification. IEEE J Select Top Appl Earth Obs Remote Sens 11(11):4141–4155CrossRef

34.

Zhou F, Hang R, Liu Q, Yuan X (2019) Hyperspectral image classification using spectral-spatial LSTMs. Neurocomputing 328:39–47CrossRef

Titel: Scalable recurrent neural network for hyperspectral image classification
verfasst von: Mercedes E. Paoletti
Juan M. Haut
Javier Plaza
Antonio Plaza
Publikationsdatum: 04.02.2020
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 11/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03187-0

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