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

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2019 by IJETT Journal
Volume-67 Issue-8
Year of Publication : 2019
Authors : Ramya T E, Marikkannan M.
  10.14445/22315381/IJETT-V67I8P216

MLA 

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).