We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Temporal Gradient based Satellite Image Compression

by Sanchita Rani Das, Md. Al Mamun, Md. Ali Hossain
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 20
Year of Publication: 2021
Authors: Sanchita Rani Das, Md. Al Mamun, Md. Ali Hossain
10.5120/ijca2021921102

Sanchita Rani Das, Md. Al Mamun, Md. Ali Hossain . Temporal Gradient based Satellite Image Compression. International Journal of Computer Applications. 174, 20 ( Feb 2021), 38-41. DOI=10.5120/ijca2021921102

@article{ 10.5120/ijca2021921102,
author = { Sanchita Rani Das, Md. Al Mamun, Md. Ali Hossain },
title = { Temporal Gradient based Satellite Image Compression },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2021 },
volume = { 174 },
number = { 20 },
month = { Feb },
year = { 2021 },
issn = { 0975-8887 },
pages = { 38-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number20/31794-2021921102/ },
doi = { 10.5120/ijca2021921102 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:22:40.685406+05:30
%A Sanchita Rani Das
%A Md. Al Mamun
%A Md. Ali Hossain
%T Temporal Gradient based Satellite Image Compression
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 20
%P 38-41
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a day’s tremendous amount of earth observing data or images is downloaded in order to accommodate in variety of geographical and environmental applications like change detection, weather forecasting, climate change, disaster management etc. The recent advancement in spatial, spectral and temporal resolution of satellite images has make it possible to use the images in these vital and world-wide challenging applications. Due to limited transmission rate, remote sensed satellite images needs to be compressed before being delivered to the user. While individual images can be processed by considering spatial and spectral redundancies, a communication system for satellite images can also consider the temporal correlation between images of same place for two different time. In this paper a gradient-based temporal compression technique has been proposed to approximate the image using a reference image of previous time, which is already available. The residual image is transmitted where the proposed rate defeats the sate of art compression technology like JPEG.

References
  1. Mamun M.A., Jia X. and Ryan M., “Non-linear Elastic Model for Flexible Prediction of Remote Sensed Multi-temporal images”. IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 5, pp. 1005 - 1009, May 2014.
  2. Cheng-Chen, L., and Yin-Tsung, H. (2010). "An Efficient Lossless Compression Scheme for Hyperspectral Images Using Two-Stage Prediction." IEEE Geoscience and Remote Sensing Letters, 7(3), 558-562.
  3. Magli E., Olmo G., and Quacchio E., "Optimized onboard lossless and near- lossless compression of hyperspectral data using CALIC." IEEE Geoscience and Remote Sensing Letters, 1(1), 21-25, 2004.
  4. Magli E., "Multiband Lossless Compression of Hyperspectral Images." IEEE Transactions on Geoscience and Remote Sensing, 47(4), 1168-1178, 2009.
  5. Wei Z., Qian D. and Fowler J.E., "Multitemporal hyperspectral image compression." IEEE Geoscience and Remote Sensing Letters, vol. 8(3), pp. 416-420, 2011.
  6. Qian D., Fowler J. E., and Wei Z., "On the Impact of Atmospheric Correction on Lossy Compression of Multispectral and Hyperspectral Imagery." IEEE Transactions on Geoscience and Remote Sensing, 47(1), 130-132, 2009.
  7. Penna B., Tillo T., Magli E., and Olmo G., "Hyperspectral Image Compression Employing a Model of Anomalous Pixels." IEEE Geoscience and Remote Sensing Letters, 4(4), 664-668, 2007.
  8. Mamun M. A., Jia X. and Hossain M. A., “Reconstruction of Satellite Images by Multi-temporal Gradient based Sequential Prediction”, IEEE Geoscience and Remote Sensing Symposium (IGARSS ‘14), pp. 1616-1618, Quebec, Canada, July 2014.
  9. Weinberger M. J., Seroussi G., and Sapiro G., "The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS." IEEE Transactions on Image Processing, 9(8), 1309-1324, 2000.
  10. Magli E., Olmo G., and Quacchio E., "Optimized onboard lossless and near- lossless compression of hyperspectral data using CALIC." IEEE Geoscience and Remote Sensing Letters, 1(1), 21-25, 2004.
  11. Hongqiang W., Babacan S. D., and Sayood K., "Lossless hyperspectral image compression using context-based conditional averages." Data Compression Conference Proceedings (DCC '05), 418-426, 2005.
  12. Magli E., "Multiband Lossless Compression of Hyperspectral Images." IEEE Transactions on Geoscience and Remote Sensing, 47(4), 1168-1178, 2009.
  13. Motulsky H. and Christopoulos A., "Fitting Models to Biological Data using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting", Oxford University Press, 2004.
  14. Mamun M. A., Jia X. and Ryan M., “Adaptive Data Compression for Efficient Sequential Transmission and Change Updating of Remote Sensing Images”, IEEE Geoscience and Remote Sensing Symposium (IGARSS ‘09), Cape Town, South Africa, p. 498-501 (IV), July 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Correlation Gradient Landsat ETM+ Multi-temporal

Powered by PhDFocusTM