This document summarizes research on compression algorithms for remote sensing images. It begins with an abstract describing the challenges of transmitting large remote sensing images from sensors to networks. The document then reviews 18 different research papers on various compression algorithms for remote sensing images, including wavelet-based algorithms, fractal coding methods, and region-based approaches. It evaluates each algorithm's performance in compressing remote sensing images while maintaining quality. The document aims to perform a comparative case study of these different compression algorithms.

1. WCECS, Vol 25, Issue 12, pp. 25-29, San Francisco, USA, 21-23, October 2015
©World Congress on Engineering and Computer Science’2015
25
A Comparative Case Study on Compression
Algorithm for Remote Sensing Images
P.S.Jagadeesh Kumar, J.Nedumaan, J.Tisa, J.Lepika
Abstract—Remote sensing and satellite images are generally
large amount of data and information. Performing study on
these remote sensing and satellite images is necessary and yet
transmitting these images from the sensors to the ground
network is challenging. In this paper, the author perspective is
to perform a comparative case study on different compression
algorithm for remote sensing and satellite images. Remote
sensing images are recorded in various wavelength and angles
of the electromagnetic spectrum. Thus transmitting them to the
ground with efficient compression algorithm is perplexing.
Index Terms—Satellite images, Compression algorithm,
Remote sensing images, Literature survey
I. INTRODUCTION
HE Remote Sensing is basically a multi-disciplinary
science which includes a combination of various
disciplines such as optics, spectroscopy, photography,
computer, electronics and telecommunication, satellite
launching etc. All these technologies are integrated to act as
one complete system in itself, known as Remote Sensing
System. There are a number of stages in a Remote Sensing
process, and each of them is important for successful
operation [1].
Depending on the application for which these images are
to be used, one may be interested only in a subset of the
imaged regions. For example, in weather forecast
application, cloud is significant while it is useless for
military observation. But, they are still cost numbers of bits
to be encoded and transmitted. It is then desirable that
intelligent processing the images, such as categorizing
images, selecting the data of interest and discarding the data
those are with minor information. Therefore, once the
regions of interest are extracted, a region-based approach
seems to be a good solution to the problem. And the last task
is to choose the coding methods that should be used for
coding interest data or the background ones. JPEG2000 is a
natural choice for its many advantages, such as allowing
lossless and lossy compression, rate control and regions of
interest coding. The next generation of remote sensing
satellites will extremely exceed their downlink capability
and conventional compression algorithms are not powerful
enough to meet the demands. Therefore, it is essential to
further develop data compression and data reduction [2].
Manuscript received January 10, 2015; revised July 29, 2015.
P.S.Jagadeesh Kumar is Professor in the School of Computer Science and
Engineering, Nanyang Technological University, Singapore.
(E-mail: dr.psjkumar@ntu.edu.sg, mobile: 09952225720).
J.Nedumaan, J.Tisa, J.Lepika is with Malco Vidyalaya Matriculation
Higher Secondary School, Mettur Dam, Salem, Tamil Nadu, and India.
II. LITERATURE SURVEY
inqi Li, Quan Zhou et al. [2] proposed a novel image
compression scheme based on classification and target
detection. In order to reduce the overall computation
complexity, some improved methods of classification and
detection are used. Their classification task accomplished
with texture analysis. The texture features based on GLCM
and gray-weighting fractal dimension show a good result in
natural and man-made regions classification. In another
hand, Hough transform is used for the detection of linear
features to identify ROI. As the original image has been
divided and classified, it achieves a notable complexity
reduction at the expenses of computation and storage.
Computer simulation results show that a good reconstructive
image in ROI region when the whole image compressed in a
high ratio.
Daljit Singh, Sukhjeet Kaur Ranade et al. [3] considered
the modular design of the scheme and various possible
cases. The non-block schemes gave better performance but
they were less computationally efficient. It was observed that
the wavelet transform gave an average around 10% PSNR
performance improvement over the DCT due to its better
energy compaction properties at very low bit rates near
about 0.25 bpp. While DCT transform gave an average
around 8% PSNR performance over wavelets at high bit
rates of 1 bpp. Wavelet transform provides good results than
DCT when more compression is required.
Yu Jie, Zhang Zhongshan et al. [4] concluded that the
fractal coding method based on wavelet domain unites the
similarity between the sub-bands after multi-resolution
analysis of wavelet and the object’s self-similar
characteristic in fractal geometry. Thereby the redundancies
in images between frequency bands are eliminated. Remote
sensing images are produced the sub-band similarity by
wavelet transform, and then fractal encoding method is used
for the adjacent sub-bands which are similar or correlative.
The steps of searching by block instead of researching by
pixel improve the image encoding efficiency. Then the
image reconstruction is effective through the high level sub-
images structuring the corresponding level sub-images.
Fractal image compression method has a potential high
compression ratio, and high-efficiency.
Ning Zhang, Longxu Jin et al. [5] admitted that the error
resilience and compression speed are improved using the
compression algorithm based on set partitioning in
hierarchical scans coefficients by the serial processing

2. WCECS, Vol 25, Issue 12, pp. 25-29, San Francisco, USA, 21-23, October 2015
©World Congress on Engineering and Computer Science’2015
26
approach. The encoding speed is limited by repeatedly
scans. A new fast SPIHT algorithm is proposed, which can
deal with all bit-planes simultaneously, and the speed is only
relative to the image resolution. The coefficients are divided
into many family blocks, stored in block RAMs separately.
The algorithm is suitable for a fast, simple hardware
implementation, and can be used in the field of aerial image
compression system, which requiring the high speed and
high error resilience.
Liu Yanyan, Li Guoning et al. [6] concluded that if the
object code rate is low, have been coding to a large number
of streaming will be discarded, eventually did not included
in the compression streaming, which causes to forsake
streaming coding waste. Therefore, the same picture in all
kinds of code rate under code JPEG2000, the complexity of
the code and the processing time almost are not significant.
For CCD image and respectively remote sensing image, in
all kinds of code rate under the detailed test, and give the
image PSNR experimental results. As contrast is given, and
the software realization JPEG2000 test results PSNR.
Compression interface card processing unit can be
compressed all the way the image data rate of 204.68 Mbps.
The actual test results show that the compression system
meet the prescribed technical index.
Hongxu Jiang, Kai Yang et al. [7] proposed a novel image
quality assessment method named MSMLD for remote
sensing image compression; it is incorporating merits of
pixel-level distortion, contexture-level distortion, content
level distortion and multiscale structural similarity.
Compared with state of the art, image quality assessment
approaches like SSIM, VIF, PSNR, and so forth, the
proposed MSMLD algorithm has a better consistency with
subjective perception values than the current state of the art
methods in remote sensing image compression assessment,
and the objective assessment results can show the distortion
features and visual quality of compressed image well. Basing
on the above, by analysing the correlation between
multilevel differences and image characteristics, six
important characteristics of image in space domain and
frequency domain are preferred, such as image gray level,
image contrast, image activity measure, wavelet coefficients
average, wavelet coefficients energy concentration, and bit-
plane entropy average. Developing a reduced reference
prediction method based on MSMLD is essential in the
future to reduce prediction complexity of image assessment
with limited information of the compressed images.
Frank Tintrup, Francesco De Natale et al. [8] tested
techniques, KLT-JPEG fulfils best the request of
compression, minimizing the degradation of CC by K-NN,
applied to multispectral remotely sensed TM images.
Moreover the other two algorithms need an updating of their
codebooks depending on the characteristics of the input
image samples, e.g. agriculture areas, urban areas and
forests. The obtained results regarding the KLT-Wavelet
algorithm are quite surprisingly as this approach is usually
more efficient against the other analyzed techniques for
many applications. The main reason for the unexpected low
efficiency of this recent technique is that the algorithm is not
yet well optimized for automatic classification of remotely
sensed images while it performs well for browsing
applications and where the visual aspect is the main goal.
Usually in these kinds of applications, the images are photos
of objects or persons and contain therefore much significant
and detailed information like for example in “Lena”.
Analyzing this image types, the WV algorithm find many
active zones which are then coded obtaining good visual
qualities also at very high compression rates. Moreover the
JPEG algorithm became already a standard based on many
years of international research in image processing including
operating techniques like DCT and Huffmann while the
recent Wavelet compression technique is not yet well
established. To take advantage of the capacity of WV coding
for these application we propose an adaptive quantization
and coding of the KL- and then Wavelet-transformed image
samples. This optimization may depend on the
characteristics of the remotely sensed images. Anyway, an
important role plays the content of the KL-Transformed
image planes where the first transformed space contains the
most information while the last transformed space is the
“poorest” image plane. The main possibilities to obtain this
optimization with the proposed Wavelet algorithm are to
vary the quantization factor in the pre-coding phase and to
adapt additionally the block size for the VQ depending on
the analyzed image plane.
Pan Wei, Zou Yf et al. [9] concluded that the classical
eight kinds of affine transformations in 2-D fractal image
compression were generalized to nineteen for the 3-D fractal
image compression. Hyperspectral image date cube was first
translated by 3-D wavelet and then the 3D fractal
compression coding was applied to lowest frequency
subband. The remaining coefficients of higher subbands
were encoding by 3-D SPIHT. They used eight-fork tree
division algorithm to improve the matching accuracy and
reduce the time of fractal coding. Through the simulation
results, show a high compression ratio while acceptable
information loss. Owing to the defect of the fractal
compression algorithm, the loss of information is biggerthan
traditional compression methods.
Jian-wei Han, Jian-dong Fang et al. [10] compared the
simulation data analysis which shows that the algorithm can
achieve effectively the remote sensing image compression
and decompression. The visual quality of the reconstruction
image compared to the original image has no distortion,
compared with the Mallat algorithm combined with EZW
coding method of reconstruction image has better visual
effects. The image PSNR of this algorithm is better to Mallat
algorithm combined with EZW coding method PSNR is
enhancing. The application of the lifting wavelet transform
to overcome the complex, time-consuming, the floating point
results of transformation in the traditional process of wavelet
transform and effective in reducing hardware complexity.
The quality of the reconstructed image is enhanced and the
hardware complexity is reduced effectively. Therefore, their
algorithm is simple, rapid and easy suitable for real-time
space remote sensing image compression.

3. WCECS, Vol 25, Issue 12, pp. 25-29, San Francisco, USA, 21-23, October 2015
©World Congress on Engineering and Computer Science’2015
27
Sun Rongchun, Chen Dianren, et al. [11] proposed a
RSI compression algorithm based on adaptive threshold.
The steps of the compression method were discussed.
Analyzing the relation between entropy and averageabsolute
value in wavelet subband, the selection method of the
adaptive threshold. The results shows that the algorithm has
the ability of adjusting CR according to the texture of the
RSI by itself and is easy to be realized in hardware duo to its
low complexity.
Bo Li, Rui Yang, Hongxu Jiang et al. [12] developed a
2D OWT and the corresponding remote sensing image
compression scheme has been presented. The proposed
transform provides a combination of oriented information
and wavelet transform that could compact more signal
features in the low frequency domain. As a result, the
compression algorithm based on their proposed transform
can achieve superior quality of decoded images. In addition,
the proposed compression scheme is suitable for remote
sensing image compression compared with the JPEG2000
and 2D OWT compression schemes. Furthermore,
combining with other algorithms, the proposed transform is
also available in the broader fields.
Sui Yuping, Yang Chengyu et al. [13] proposed a novel
lossless compression algorithm of remote sensing image for
space applications. It can adapt to the changes in data
statistics, make data packet independent with each other.
The algorithm also can limit error propagation in a way.
Compared with JPEG 2000, this approach has similar CR,
furthermore, it has lower complexity, which may be suitable
for space applications.
M. Cagnazzo, G. Poggi et al. [14] concluded that the
region-based approach seems very well suited to the
compression of multispectral and hyperspectral images.
Although there is certainly room for further improvements,
the proposed coding scheme using shape-adaptive wavelet
transform and SPIHT is already superior to state of the art
techniques, and represents a more flexible and feature rich
coding tool. Another interesting feature of the region-based
approach is the freedom to allocate more resources to
regions of interest. Of course, the other parts of the image
will be barely encoded, but this could not be a problem for
applications where the user is only interested on a particular
region, maybe selected only by looking at the map.
D.Sophin Seeli, M.K.Jeyakumar et al. [15] successful
implemented the fractal coder for different imaging
modalities. Fractal coders can perform very well in terms of
bit rate and PSNR for satellite images. The most important
feature of Fractal Decoding is the high image quality when
zooming in/out on the decoded picture. Fractal images
compression has the characteristics of high compression
ratio, fast decoding and long coding time. From the analysis
carried out, the fractal coding techniques can be applied for
achieving high compression ratios and better peak signal to
noise ratio values for satellite images. Further improvement
can be attained by considering the different dimensions of
the range blocks and optimizing number of iterations. This
type of compression can be applied in satellite band images,
where it is needed to focus on image details, and in
Surveillance Systems, when trying to get a clear picture of
the intruder.
Wei Hua, Rui Wang et al. [16] presented a systematic
solution to efficiently compress and decompress multiple
largescale remote sensing images for geographic
visualization applications problem in the image factorization
framework. The system uses a set of codebooks for different
purposes, so that each of them can built in a compact and
efficient way. They also proposed a GSR descriptor to
accelerate the similarity search and demonstrated that
similarity matches can be efficiently found in the GSR
space. Finally improved the compression quality by
compressing the error map using a customized S3TC like
compression algorithm. Looking into the future, they would
like to further improve the compression speed by
implementing compression algorithms on GPU. Also their
interest is in applying different importance weights to local
image regions, so that adaptively control the local
compression quality. To assemble codebooks into a compact
form is also an interesting topic to study in the future.
Alaitz Zabala, Xavier Pons et al. [17] suggested that
according to classification results for mapping Crops and
forest areas, J2K format is better than JPG for forests but not
for crops. Nevertheless, according to visual effects,
classification obtained from J2K compressed images has less
salt and pepper effect and from a cartographic perspective,
J2K approach is much more effective. In accordance with
the presented results, a general trend may be set about
greater sensibility of crops to compression, thus they have to
be preserved from heavy compression. It is also important to
point out that in borders between highly differentiated
spectral classes compression produces mixing effects that
lead to errors in these areas. These border effects will have a
marked impact on future studies of changes in land use
which may produce masked results due to the erroneous
classification of the border areas. This is aggravated by the
virtual non-existence of test points in these areas, which will
hide the decrease in global accuracy. Future work will be
aimed to obtain more results to better define the relationship
between CR and accuracy or classified area, especially for
crop areas. The same aim may be achieved studying
accuracy and classified area results for different sets of
classifications using other parameters. Finally another
accuracy estimation method may be also tested.
Mallikarjun bankapur et al. [18] discussed block adaptive
quantization (BAQ) algorithm, amplitude and phase
compression (AP) algorithm and wavelet BAQ (WT-BAQ)
algorithm and wavelet packet BAQ (WPT-BAQ) algorithm.
Considering the statistical independent property between
amplitude and phase of raw data along with the growing
popularity of wavelets, two additional algorithms are
presented: wavelet AP (WT-AP) algorithm and wavelet
packet AP (WPT-AP) algorithm. The six different
algorithms are compared in image domain with several
quality parameters and the simulation is given to validate

4. WCECS, Vol 25, Issue 12, pp. 25-29, San Francisco, USA, 21-23, October 2015
©World Congress on Engineering and Computer Science’2015
28
analytic result. And found that comparing all the techniques
implemented, it is proved that, WT- BAQ algorithm and
WT-AP have the least MSE and highest PSNR and found to
be the better compression algorithms when compared to rest
of the compression algorithms.
Gopal K. Patidar, Krishna Patidar et al. [19] concluded
that the DWT–FFT scheme performs better than SPIHT in
compression of SAR images. The proposed algorithm shows
significant improvement in PSNR and MSE as compared to
SPIHT. Significant improvement in metrics with increase in
BPP was noticed. They mainly analysed the SAR data from
various satellite projections. These results may prove useful
in further analysis of non-stationary signals, time series, etc.
P. Ghamisi, A. Mohammadzadeh et al. [20] proposed an
efficient method for lossless compression of LiDAR
rasterized data and RS grayscale images. The proposed
method is suitable for real time applications. As the
hardware implementation accelerates the real time
applications process, suitability satisfied by introducing a
low complex and fast algorithm. However, compression
ratio of the proposed method is more powerful than few
previous methods such as lossless JPEG and JPEG2000 in
both of RS grayscale images and LiDAR rasterized data.
The compression ratio improvement helps transmission
systems to work faster and helps the real time process. The
proposed method is based on Enhanced DPCM
Transformation (EDT) and optimized Huffman entropy
encoder. The future studies would be on, hardware
implementation, testing and evaluation.
D.Napoleon, S.Sathya et al. [21] compared JPEG2000
and 3D-OWT compression scheme for the compression of
remote sensing images. The compression algorithm based on
the proposed transform can achieve superior quality of
decoded images. The proposed transform provides a
combination of oriented information and wavelet transform.
3D-OWT has similar difficulty as like the separable wavelet
transform while providing better energy compaction and
staying critically sampled, which makes it a good candidate
for compression applications. Our experiment shows as 3D-
OWT compression scheme is suitable for remote sensing
image compression compared with the JPEG2000.
R.Balasubramanian et al. [22] concluded that the
classified output of the uncompressed and compressed
remote sensing images resembles the same using the wavelet
applications shows it is lossless. Wavelet techniques can be
employed for compression to save the bandwidth
requirements for data transmission of precision images. The
study area image was rectified and pre-processed. Then
compression technique was employed. Both the compressed
and uncompressed images were imported to the image
processing software for marking training set samples for
various classes like beach, vegetation, water bodies, urban
with and without vegetation. The training set selections of
various features are sampled and maximum likelihood
technique is adopted for classification.
Pedram Ghamisia et al. [23] developed an efficient
method for lossless compression of a wide variety of RS
images. The proposed EDT based compression method is
more efficient for all types of images tested. Large
improvements in the compression ratio over the Lossless
JPEG and JPEG2000 methods were achieved. In other
words, the new method has a great potential to compress
different types of images in less CPU computational time.
The proposed method is based on an enhanced DPCM
transformation and an optimized Huffman entropy encoder.
Future studies would look at hardware implementation,
testing and evaluation.
Wang Qingyuan et al. [24] employed neighborhood
correlation and spacial correlation in wavelet domain more
sufficiently. A new context model is proposed by utilizing
eight immediate neighbour coefficients and parent coefficient.
According to their prediction influence, different weight values
are assigned to them. Based on minimum conditional entropy
and a large amount of compression experiments, seven context
labels are constructed. Experimental results show that the new
model can improve remote sensing image compression
performance, which proves the model can sufficiently exploit
high order correlation and reduce entropy redundancy.
III. CONCLUSION
It is worth noticing that plenty of compression algorithms are
available for compressing remote sensing images. Which
compression algorithm is the best for remote sensing images?
It is not so easy question to answer. But still based on the case
study done in this paper, it is observed that certain algorithms
are suitably good for certain applications. The following points
could be summarized based on the case study done in this
paper;
1) Wavelet transform provides better compression and
PSNR ratio compared to DCT for lossy compression of
remote sensing and satellite images.
2) JPEG2000 algorithm is efficient in compressing natural
images but when comes to remote sensing images; a
hybrid extension coder is required to classify or
distinguish the pigments in the image which are supposed
to taken at different wavelength, angle and climatic
conditions.
3) More efficient new algorithms are yet to be identified in
the future, based on the technological development in the
area of image compression and SAR images.
IV. ACKNOWLEDGEMENT
Our sincere thanks to late Dr.APJ Abdul Kalam, Ex-President,
India, who have been a motivation and inspiration to millions
of researchers, teachers, students and peoples.
REFERENCES
[1] Shefali Aggarwal, “Principles of remote sensing”, Satellite Remote
Sensing and GIS Applications in Agricultural Meteorology pp. 23-38.
[2] Minqi Li, Quan Zhou, and Jun Wang, “Remote Sensing Image
Compression Based on Classification and Detection,” PIERS
Proceedings, Hangzhou, China, March 24-28, 2008, pp.564-568.

5. WCECS, Vol 25, Issue 12, pp. 25-29, San Francisco, USA, 21-23, October 2015
©World Congress on Engineering and Computer Science’2015
29
[3] Daljit Singh, Sukhjeet Kaur Ranade, “Comparative Analysis of
Transform based Lossy Image Compression Techniques,”
International Journal of Engineering Research and Applications, Vol.
2, Issue 5, September- October 2012, pp.1736-1741.
[4] P.S.Jagadeesh Kumar, Krishna Moorthy, “Intelligent Parallel
Processing and Compound Image Compression”, Advances in Parallel
Computing, New Frontiers in Computing and Communications 2013,
Vol. 38, Issue 1, January 2013, pp.196-205.
[5] Ning Zhang, Longxu Jin, Yinhua Wu, Ke Zhang, “An Improved Fast
SPIHT Image Compression Algorithm for Aerial Applications”,
Journal of multimedia, Vol. 6, no. 6, December 2011, pp.494-501.
[6] Liu Yanyan, Li Guoning, “Wavelet Transform in Remote Sensing
Image Compression of the Key Technical Analysis”, Modern Applied
Science Vol. 6, No. 4; April 2012, pp.44-48.
[7] Hongxu Jiang, Kai Yang, Tingshan Liu, and Yongfei Zhang, “Quality
Prediction of DWT-Based Compression for Remote Sensing Image
Using Multiscale and Multilevel Differences Assessment Metric”,
Mathematical Problems in Engineering Volume 2014, Article ID
593213, 15 pages, http://dx.doi.org/10.1155/2014/593213.
[8] Frank Tintrup, Francesco De Natale, Daniele Giusto, “Compression
algorithms for classification of remotely sensed images”, 0-7803-
4428-6/98, 1998 IEEE, pp.2565-2568.
[9] Pan Wei, Zou Yf, Ao Lu, “A Compression Algorithm of
Hyperspectral Remote Sensing Image Based on 3-D Wavelet
Transform and Fractal”, Proceedings of 2008 3rd International
Conference on Intelligent System and Knowledge Engineering, 978-
1-4244-2197-8/08©2008 IEEE, pp.1237-1241.
[10] Jian-wei Han, Jian-dong Fang, “Research on Remote Sensing Image
Compression Based on Lifting Wavelet Transform”, International
Conference on Multimedia and Information Technology, 978-0-7695-
3556-2/08© 2008 IEEE, pp.205-208, DOI 10.1109/MMIT.2008.22.
[11] Sun Rongchun, Chen Dianren, Li Xingguang, Wang Xin, “A Remote
Sensing Image Compression Algorithm Based on Adaptive
Threshold”, 3rd International Symposium on Intelligent Information
Technology Application Workshops, 978-0-7695-3860-0/09©2009
IEEE, DOI 10.1109/IITAW.2009.115, pp.376-378.
[12] P.S.Jagadeesh Kumar, “A Study on the Compatibility of Hybrid
Approaches to Satellite Image Compression”, Image and Vision
Computing, 23(8/2), December 2005, pp. 776-783.
[13] Sui Yuping, Yang Chengyu, Liu Yanjun, Wang Jun, Wei Zhonghui
He Xin, “A lossless compression algorithm of remote sensing image
for space applications”, Journal of Electronics (China), Vol.25 No.5,
September 2008, pp.647-651.
[14] M. Cagnazzo, G. Poggi, L. Verdoliva, A. Zinicola, “Region-oriented
compression of multispectral images by shape-adaptive wavelet
transform and SPIHT”, -7803-8554-3/04©2004IEEE, pp.2459-2462.
[15] D.Sophin Seeli, M.K.Jeyakumar, “Performance Assessment of Fractal
Coding on Remote Sensing Images with Different Imaging
Modalities”, International Journal of Engineering and Advanced
Technology, Volume-2, Issue-6, August 2013, pp.230-235.
[16] Wei Hua, RuiWang, Xusheng Zeng, Ying Tang, HuaminWang,
Hujun Bao, “Compressing repeated content within large-scale remote
sensing images”, Springer-Verlag 2012, Vis Comput, 24 April, 2012,
DOI 10.1007/s00371-012-0710-3
[17] Alaitz Zabala, Xavier Pons, Ricardo Díaz-Delgado, “Effects of JPEG
and JPEG2000 Lossy Compression on Remote Sensing Image
classification for Mapping Crops and Forest areas”, Unpublished.
[18] Mallikarjun Bankapur, Manjula, “Development of Compression
Algorithms for Remotely Sensed SAR Data”, International Journal of
Engineering Science and Innovative Technology (IJESIT) Volume 2,
Issue 4, July 2013, pp.443-450.
[19] Gopal K. Patidar, Krishna Patidar, Swapna Pillai, Tarwar S. Savner,
M. Chiranjeevi, “An Improved Technique for Complex SAR Image
Compression Based on FFT and Discrete Wavelet Transform”,
International journal of innovative research in electrical, electronics,
instrumentation and control engineering, Vol. 2, Issue 9, September
2014, pp.1984-1988.
[20] P. Ghamisi, A. Mohammadzadeh, M. R. Sahebi, F. Sepehrband, J.
Choupan, “A Novel Real Time Algorithm for Remote Sensing
Lossless Data Compression based on Enhanced DPCM”,
International Journal of Computer Applications, Volume 27– No.1,
August 2011, pp.47-53.
[21] P.S.Jagadeesh Kumar, “Pioneering VDT Image Compression using
Block Coding”, IEE Proceedings - Vision, Image and Signal
Processing, 152 (4), August 2005, pp. 513-518.
[22] Balasubramanian R, Ramakrishnan SS, “Wavelet application in
compression of a remote sensed image”, International Conference on
Remote Sensing, Environment and Transportation Engineering,
Published by Atlantis Press, 2013, 66-664.
[23] Pedram Ghamisia, Farshid Sepehrbandb, Lalit Kumarc, Micael S.
Couceirod, Fernando M.L. Martins, “A new method for compression
of remote sensing images based on an enhanced differential pulse
code modulation transformation”, Science Asia 39 (2013): pp.546–
555, DOI:10.2306/scienceasia1513-1874.2013.39.546.
[24] Wang Qingyuan, Li Tao, Wang Kun, “Context based wavelet image
compression algorithm for remote sensing image”, Unpublished.
CORRESPONDING AUTHOR’S BIOGRAPHY
P.S.Jagadeesh Kumar received his B.E degree from the
University of Madras in Electrical and Electronics
Engineering discipline in the year 1999. He obtained his
MBA degree in HRD from University of Strathclyde,
Glasgow, and the United Kingdom in the year 2002. He
obtained his M.E degree in 2004 with specialization in
Computer Science and Engineering from Annamalai
University, Chidambaram. He further achieved his M.S
Degree in Computer Engineering from New Jersey Institute of Technology,
Newark, and the USA in the year 2006 and his Doctorate from the
University of Cambridge, United Kingdom in the year 2013. He started his
career as Assistant Professor in the Department of Electrical and Computer
Engineering, Carnegie Mellon University, Pennsylvania, United States and
continued to render his service as Associate Professor in the Department of
Computer Science, Faculty of Computer Science and Technology,
University of Cambridge, Cambridge, United Kingdom. At present, he is
working as Professor at the School of Computer Science and Engineering
for Nanyang Technological University, Singapore.