Investigations on Combinational Approach for Processing Remote Sensing Images Using Deep Learning Techniques

Ramya T E; Marikkannan M.

doi:https://doi.org/10.14445/22315381/IJETT-V67I8P216

Research Article | Open Access | Download PDF

Volume 67 | Issue 8 | Year 2019 | Article Id. IJETT-V67I8P216 | DOI : https://doi.org/10.14445/22315381/IJETT-V67I8P216

Investigations on Combinational Approach for Processing Remote Sensing Images Using Deep Learning Techniques

Ramya T E, Marikkannan M.

Citation :

Ramya T E, Marikkannan M., "Investigations on Combinational Approach for Processing Remote Sensing Images Using Deep Learning Techniques," International Journal of Engineering Trends and Technology (IJETT), vol. 67, no. 8, pp. 92-97, 2019. Crossref, https://doi.org/10.14445/22315381/IJETT-V67I8P216

Abstract

Deep learning (DL) techniques are becoming important to solve a number many of image processing tasks. Among common algorithms, the convolutional neural network and recurrent neural network-based systems achieves the state-of-the- art results on satellite and aerial imagery in many applications. While these approaches are subjected to the scientific interest, there is currently a no operational and generic implementation available at the user level for the remote sensing (RS) community. In this paper, we propose a framework whichenablesthe use of DL techniques with RS images and geospatial data. The results takes roots in two extensively used open-source libraries namely, the RS image processing library Orfeo ToolBox and the high-performance numerical computation library TensorFlow. Though ,it can be capable to apply deep nets without restriction on image size and is found computationally efficient, regardless of hardware configuration.

Keywords

Aerial images, deep learning (DL), neural networks, Orfeo Toolbox (OTB), remote sensing (RS),Tensor Flow (TF).

References

[1] X. X. Zhu et al. (2017). ?Deep learning in remote sensing: A review.? [Online]. Available: https://arxiv.org/abs/1710.03959
[2] D. H. T. Minhet al., ?Deep recurrent neural networks for winter vegetation quality mapping via multitemporal SAR sentinel-1,? IEEE Geosci. Remote Sens. Lett., vol. 15, no. 3, pp. 464–468, Mar. 2018.
[3] L. Wang, K. A. Scott, L. Xu, and D. A. Clausi, ?Sea ice concentration estimation during melt from dual-pol sar scenes using deep convolutional neural networks: A case study,? IEEE Trans. Geosci. Remote Sens., vol. 54, no. 8, pp. 4524–4533, Aug. 2016.
[4] W. Li, G. Wu, and Q. Du, ?Transferred deep learning for anomaly detection in hyper-spectral imagery,? IEEE Geosci. Remote Sens. Lett., vol. 14, no. 5, pp. 597–601, May 2017.
[5] M. Volpi and D. Tuia, ?Dense semantic labeling of subdecimeter resolution images with convolutional neural networks,? IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 881–893, Feb. 2016.
[6] E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, ?Convolutional neural networks for large-scale remote-sensing image classification,? IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp.645–657, Feb. 2017.
[7] G. Cheng and J. Han, ?A survey on object detection in optical remote sensing images,? ISPRS J. Photogramm. Remote Sens., vol. 117, pp. 11–28, Jul. 2016.
[8] P. Napoletano, ?Visual descriptors for content-based retrieval of remote- sensing images,? Int. mote Sens., vol. 39, no. 5, pp. 1343–1376, 2018.
[9] D. Ienco, R. Gaetano, C. Dupaquier, and P. Maurel, ?Land cover classification via multitemporal spatial data by deep recurrent neural networks,? IEEE Geosci. Remote Sens. Lett., vol. 14, no. 10, pp. 1685–1689, Oct. 2017.
[10] G. Masi, D. Cozzolino, L. Verdoliva, and G. Scarpa, ?Pansharpening by convolutional neural networks,? Remote Sens., vol. 8, no. 7, p. 594, Jul. 2016.
[11] K. Nogueira, O. A. B. Penatti, and J. A. dos Santos, ?Towards better exploiting convolutional neural networks for remote sensing scene classification,? Pattern Recognit., vol. 61, pp. 539–556, Jan. 2017.
[12] D. Marmanis, M. Datcu, T. Esch, and U. Stilla, ?Deep learning Earth observation classification using Image Net pretrained networks,? IEEE Geosci. Remote Sens. Lett., vol. 13, no. 1, pp. 105–109, Jan. 2016.
[13] M. Grizonnet, J. Michel, V. Poughon, J. Inglada, M. Savinaud, and R. Cresson, ?Orfeo ToolBox: Open source processing of remote sensing images,? Open Geospatial Data, Softw. Standards, vol. 2, no. 1, p. 15, 2017.
[14] M. Abadi et al., ?TensorFlow: A system for large-scale machine learning,? in Proc. OSDI, vol.16. 2016, pp. 265–283.
[15] J. Inglada, A. Vincent, M. Arias, B. Tardy, D. Morin, and I. Rodes, ?Operational high resolution land cover map production at the country scale using satellite image time series,? Remote Sens., vol. 9, no. 1, p. 95, 2017.
[16] Potsdam Dataset. Accessed:Apr.30,2018. [Online]. Available: www2. isprs.org/commissions/comm3/wg4/2d-sem-label-potsdam.html
[17] OTBTensorflow Remote Module for Orfeo Toolbox. Accessed: May 11, 2018. [Online]. Available: https://github.com/remicres/otbtf