Investigations on Combinational Approach for Processing Remote Sensing Images Using Deep Learning Techniques
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2019 by IJETT Journal | ||
| Volume-67 Issue-8 |
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| Year of Publication : 2019 | ||
| Authors : Ramya T E, Marikkannan M. |
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| DOI : 10.14445/22315381/IJETT-V67I8P216 |
Citation
MLA Style: 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 67.8 (2019):87-91.
APA Style: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, 67(8),87-91.
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.
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Keywords
Aerial images, deep learning (DL), neural networks, Orfeo Toolbox (OTB), remote sensing (RS),Tensor Flow (TF).