scholarly journals Reconstruction Technique Based on the Theory of Compressed Sensing Satellite Images

2015 ◽  
Vol 9 (1) ◽  
pp. 74-81
Author(s):  
Wang Feng ◽  
Chen Feng-wei ◽  
Wang Jia
Keyword(s):  
Compressed Sensing ◽  
Satellite Images ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Satellite Image ◽  
Technical Problem ◽  
Reconstruction Technique ◽  
Satellite Image Processing ◽  
Matching Pursuit Algorithm ◽  
Compression Sensing

Owing to the characteristics such as high resolution, large capacity, and great quantity, thus far, how to efficient store and transmit satellite images is still an unsolved technical problem. Satellite image Compressed sensing (CS) theory breaks through the limitations of traditional Nyquist sampling theory, it is based on signal sparsity, randomness of measurement matrix and nonlinear optimization algorithms to complete the sampling compression and restoring reconstruction of signal. This article firstly discusses the study of satellite image compression based on compression sensing theory. It then optimizes the widely used orthogonal matching pursuit algorithm in order to make it fits for satellite image processing. Finally, a simulation experiment for the optimized algorithm is carried out to prove this approach is able to provide high compression ratio and low signal to noise ratio, and it is worthy of further study.

Array DOA Estimation by the Model of Four Elements in Quare Array Based on the Theory of Compressed Sensing

2014 ◽  
Vol 635-637 ◽  
pp. 971-977 ◽  
Author(s):  
Rui Yuan ◽  
Jun Yue ◽  
Hong Xiu Gao
Keyword(s):  
Computer Simulation ◽  
Computational Complexity ◽  
Compressed Sensing ◽  
Matching Pursuit ◽  
Doa Estimation ◽  
Low Computational Complexity ◽  
Matching Pursuit Algorithm ◽  
Square Array

The DOA estimation by the model of four elements in the square array has studied based on the theory of compressed sensing. Using matching pursuit algorithm and orthogonality matching pursuit algorithm, the computer simulation was presented. The results show the method of DOA estimation by compressed sensing theory is simple, practical and low computational complexity.

scholarly journals A Low-Complexity Compressed Sensing Reconstruction Method for Heart Signal Biometric Recognition

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5330
Author(s):  
Xiao ◽  
Hu ◽  
Shao ◽  
Li
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Low Complexity ◽  
Portable Devices ◽  
Reconstruction Method ◽  
Signal Recognition ◽  
Bioelectric Signal ◽  
Increasing Demand ◽  
Feature Values ◽  
Compression Sensing

Biometric systems allow recognition and verification of an individual through his or her physiological or behavioral characteristics. It is a growing field of research due to the increasing demand for secure and trustworthy authentication systems. Compressed sensing is a data compression acquisition method that has been proposed in recent years. The sampling and compression of data is completed synchronously, avoiding waste of resources and meeting the requirements of small size and limited power consumption of wearable portable devices. In this work, a compression reconstruction method based on compression sensing was studied using bioelectric signals, which aimed to increase the limited resources of portable remote bioelectric signal recognition equipment. Using electrocardiograms (ECGs) and photoplethysmograms (PPGs) of heart signals as research data, an improved segmented weak orthogonal matching pursuit (OMP) algorithm was developed to compress and reconstruct the signals. Finally, feature values were extracted from the reconstructed signals for identification and analysis. The accuracy of the proposed method and the practicability of compression sensing in cardiac signal identification were verified. Experiments showed that the reconstructed ECG and PPG signal recognition rates were 95.65% and 91.31%, respectively, and that the residual value was less than 0.05 mV, which indicates that the proposed method can be effectively used for two bioelectric signal compression reconstructions.

scholarly journals Fuzzy C-Means Algorithm Based Satellite Image Segmentation

2018 ◽  
Vol 9 (2) ◽  
pp. 332 ◽  
Author(s):  
Syed Nazeebur Rehman ◽  
Mohameed Ali Hussain
Keyword(s):  
Satellite Images ◽  
Semantic Information ◽  
Signal To Noise Ratio ◽  
Satellite Image ◽  
Human Visual Perception ◽  
Pixel Intensity ◽  
Fcm Algorithm ◽  
Prior Probabilities ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

In this paper, an improved version of Fuzzy C-Means (FCM) algorithm is proposed efficiently to segment the satellite images. Segmentation of Image is one of the promising and active researches in recent years. As literature prove that region segmentation will produce better results. Human visual perception is more effective than any machine vision systems for extracting semantic information from image. A FCM algorithm is developed to estimate parameters of the prior probabilities and likelihood probabilities. So FCM algorithm is used for segmenting background and island extraction is done based on pixel intensity. Finally Peak Signal to Noise Ratio (PSNR) is calculated and it has better results than other.

scholarly journals A WEB-BASED VIRTUAL LABORATORY FOR SATELLITE IMAGE PROCESSING AND ANALYSIS

2018 ◽  
Vol XLII-5 ◽  
pp. 9-13
Author(s):  
K. M. Buddhiraju ◽  
L. N. Eeti ◽  
K. K. Tiwari
Keyword(s):  
Image Processing ◽  
System Architecture ◽  
Satellite Images ◽  
Satellite Image ◽  
Virtual Laboratory ◽  
Web Based ◽  
Continuous Increase ◽  
Image Processing And Analysis ◽  
The World ◽  
Satellite Image Processing

<p><strong>Abstract.</strong> With continuous increase in the utilization of satellite images in various engineering and science fields, it is imperative to equip students with additional educational aid in subject of satellite image processing and analysis. In this paper a web-based virtual laboratory, which is accessible via internet to anyone around the world with no cost or constraints, is presented. Features of the laboratory has been discussed in addition to details regarding system architecture and its implementation. Virtual laboratory is tested by students, whose responses are also presented in this paper. Future development of this laboratory is outlined in the end.</p>

scholarly journals Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 257
Author(s):  
Renjie Xu ◽  
Ting Yun ◽  
Lin Cao ◽  
Yunfei Liu
Keyword(s):  
Compressed Sensing ◽  
Matching Pursuit ◽  
Laser Scanner ◽  
Point Clouds ◽  
Reconstruction Algorithms ◽  
Morphological Attributes ◽  
Forest Inventories ◽  
Matching Pursuit Algorithm ◽  
Fourier Matrix ◽  
Coordinate Data

The terrestrial laser scanner (TLS) has been widely used in forest inventories. However, with increasing precision of TLS, storing and transmitting tree point clouds become more challenging. In this paper, a novel compressed sensing (CS) scheme for broad-leaved tree point clouds is proposed by analyzing and comparing different sparse bases, observation matrices, and reconstruction algorithms. Our scheme starts by eliminating outliers and simplifying point clouds with statistical filtering and voxel filtering. The scheme then applies Haar sparse basis to thin the coordinate data based on the characteristics of the broad-leaved tree point clouds. An observation procedure down-samples the point clouds with the partial Fourier matrix. The regularized orthogonal matching pursuit algorithm (ROMP) finally reconstructs the original point clouds. The experimental results illustrate that the proposed scheme can preserve morphological attributes of the broad-leaved tree within a range of relative error: 0.0010%–3.3937%, and robustly extend to plot-level within a range of mean square error (MSE): 0.0063–0.2245.

scholarly journals Image Processing Techniques for Analysis of Satellite Images for Historical Maps Classification—An Overview

2020 ◽  
Vol 10 (12) ◽  
pp. 4207 ◽  
Author(s):  
Anju Asokan ◽  
J. Anitha ◽  
Monica Ciobanu ◽  
Andrei Gabor ◽  
Antoanela Naaji ◽  
...  
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Satellite Image ◽  
Historical Maps ◽  
Satellite Imaging ◽  
Image Processing Techniques ◽  
Innovative Solutions ◽  
Satellite Image Processing ◽  
Processing Techniques ◽  
Selection Of

Historical maps classification has become an important application in today’s scenario of everchanging land boundaries. Historical map changes include the change in boundaries of cities/states, vegetation regions, water bodies and so forth. Change detection in these regions are mainly carried out via satellite images. Hence, an extensive knowledge on satellite image processing is necessary for historical map classification applications. An exhaustive analysis on the merits and demerits of many satellite image processing methods are discussed in this paper. Though several computational methods are available, different methods perform differently for the various satellite image processing applications. Wrong selection of methods will lead to inferior results for a specific application. This work highlights the methods and the suitable satellite imaging methods associated with these applications. Several comparative analyses are also performed in this work to show the suitability of several methods. This work will help support the selection of innovative solutions for the different problems associated with satellite image processing applications.

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