Open Access Journal

ISSN : 2394 - 6849 (Online)

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering

Open Access Journal

International Journal of Engineering Research in Electronics and Communication Engineering(IJERECE)

Monthly Journal for Electronics and Communication Engineering
Call For Paper : Vol 11, Issue 03, March 2024

ISSN : 2394-6849 (Online)

Call for Paper

Vol 11, Issue 03, March 2024

Indexing

Implementation of Convolution Neural Network in Processing of Satellite Video

Author : Ankitha Venkatesh 1 Jami Sai Bandhavi 2 Asha R 3 B Nagarushitha 4 Vinod B Durdi 5

PDF

Date of Publication :25th May 2018

Abstract: The video sent by satellite usually suffers severe degradation due to hardware imperfections or uncontrollable acquisition conditions. So there is a lot of scope in video processing-enhancement and reconstruction.The quality of image is improved by using Super-resolution. The main aim of satellite video processing is to obtain good Peak Signal to Noise Ratio (PSNR) and resolution of the stream of images of the video. To achieve this, Neural Networks are employed. Convolution Neural Networks is a part of Neural Networks which has been proven to be effective in areas of image processing

Reference :

    1. Yimin Luo, Liguo Zhou, Shu Wang, and Zhongyuan Wang,” Video Satellite Imagery Super Resolution via Convolutional Neural Networks,” IEEE,VOL. 14, NO. 12, Dec 2017
    2. J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process., vol. 19, no. 11, pp. 2861–2873, Nov. 2010.
    3. S. Wang, L. Zhang, Y. Liang, and Q. Pan, “Semicoupled dictionary learning with applications to image super-resolution and photo-sketch synthesis,” in Proc. CVPR, Providence, RI, USA, Jun. 2012,pp. 2216–2223.
    4. S. Schulter, C. Leistner, and H. Bischof, “Fast and accurate image upscaling with super-resolution forests,” in Proc. CVPR, Boston, MA, USA, 2015, pp. 3791–3799.
    5. S. Ji, W. Xu, M. Yang, and K. Yu, “3D convolutional neural networks for human action recognition,” IEEE Trans. Pattern Anal. Mach. Intell, vol. 35, no. 1, pp. 221–231, Jan. 2013.
    6. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. ICLR, San Diego, CA, USA, 2015, pp. 1–14.[4] Y. Wen, K. Zhang, Z. Li, and Y. Qiao, “A discriminative feature learning approach for deep face recognition,” in Proc. ECCV, Amsterdam, The Netherlands, 2016, pp. 499– 515.
    7. P. E. Dennison, A. R. Brunelle, and V. A. Carter, “Assessing canopy mortality during a mountain pine beetle outbreak using GeoEye-1 high spatial resolution satellite data,” Remote Sens. Environ., vol. 114, no. 11, pp. 2431– 2435, 2010
    8. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. NIPS, Lake Tahoe, NV, USA, 2012, pp. 1097–1105.
    9. J. Kim, J. K. Lee, and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proc. CVPR, Boston, MA, USA, 2015, pp. 1646–1654.
    10. Q. Cheng, H. Liu, H. Shen, P. Wu, and L. Zhang, “A spatial and temporal nonlocal filter-based data fusion method,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 8, pp. 4476–4488, Aug. 2017.

Recent Article