scholarly journals The Multiscale Image Processing Method from On-board Earth Remote Sensing Systems Based on the Artificial Bee Colony Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 2557-2562 ◽  
Author(s):  
Hennadii Khudov
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Processing Method ◽  
Earth Remote Sensing ◽  
Image Processing Method ◽  
Bee Colony ◽  
Sensing Systems ◽  
Remote Sensing Systems

scholarly journals An Efficient Framework for Remote Sensing Parallel Processing: Integrating the Artificial Bee Colony Algorithm and Multiagent Technology

2019 ◽  
Vol 11 (2) ◽  
pp. 152 ◽  
Author(s):  
Lina Yang ◽  
Xu Sun ◽  
Zhenlong Li
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Parallel Computing ◽  
Artificial Bee Colony Algorithm ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Endmember Extraction ◽  
Abc Algorithm ◽  
Computational Burden ◽  
Bee Colony

Remote sensing (RS) image processing can be converted to an optimization problem, which can then be solved by swarm intelligence algorithms, such as the artificial bee colony (ABC) algorithm, to improve the accuracy of the results. However, such optimization algorithms often result in a heavy computational burden. To realize the intrinsic parallel computing ability of ABC to address the computational challenges of RS optimization, an improved multiagent (MA)-based ABC framework with a reduced communication cost among agents is proposed by utilizing MA technology. Two types of agents, massive bee agents and one administration agent, located in multiple computing nodes are designed. Based on the communication and cooperation among agents, RS optimization computing is realized in a distributed and concurrent manner. Using hyperspectral RS clustering and endmember extraction as case studies, experimental results indicate that the proposed MA-based ABC approach can effectively improve the computing efficiency while maintaining optimization accuracy.

scholarly journals Materials for Test Objects to Configure Aviation Optoelectronic Earth Remote Sensing Systems

2017 ◽  
Vol 99 ◽  
pp. 01001 ◽  
Author(s):  
E.B. Bablyuk ◽  
Yu.M. Berlad ◽  
A.G. Letyago ◽  
A.P. Kondratov
Keyword(s):  
Remote Sensing ◽  
Earth Remote Sensing ◽  
Sensing Systems ◽  
Remote Sensing Systems ◽  
Test Objects

scholarly journals Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

2021 ◽  
Vol 333 ◽  
pp. 01011
Author(s):  
Assiya Sarinova
Keyword(s):  
Remote Sensing ◽  
Communication Channel ◽  
Remote Sensing Data ◽  
Discrete Cosine Transformation ◽  
Earth Remote Sensing ◽  
Compression Algorithms ◽  
Sensing Systems ◽  
Discrete Orthogonal ◽  
Remote Sensing Systems ◽  
Aerospace Images

The paper describes the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table has been developed. The conducted studies have shown that the proposed lossy algorithms have sufficient efficiency for use and can be applied when transmitting hyperspectral remote sensing data in conditions of limited buffer memory capacity and bandwidth of the communication channel.

Exploration of Remote Sensing Image Processing Method for Glacial Geomorphology Research

2012 ◽  
Vol 500 ◽  
pp. 437-443
Author(s):  
Shang Min Zhao ◽  
Wei Ming Cheng ◽  
Xi Chen
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Remote Sensing Image ◽  
Transformation Method ◽  
Research Area ◽  
Processing Method ◽  
Glacial Geomorphology ◽  
Image Processing Method ◽  
Remote Sensing Image Processing ◽  
Selection Of

Taking Mt. Namjagbarwa region as an example, this paper explores a complete remote sensing image processing method for glacial geomorphology research. Based on the selection of Landsat7 ETM+ images, the remote sensing image processing method such as band selection, overlap, fusion, mosaic and so on is carried out. The result shows: ① right selection of remote sensing images and proper process based on the characteristics of research area and research purpose, not only reduce the process difficulty, but make a firm foundation for subsequent glacial geomorphology research; ②according to the computation of correlation coefficient between fusion images and original multi-spectral images, panchromatic high resolution images, the result shows that the principle component transformation method has better effect than IHS transformation method in remote sensing image fusion process.

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