A Study and Design of Compressed Encoder Based on Neural Network for Remote Sensing Image

Ling Tao; Wen Long Li; Yan Yan Yu; Jian Kang; Jun Yang

doi:10.4028/www.scientific.net/AMR.989-994.4100

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994A Study and Design of Compressed Encoder Based on...

A Study and Design of Compressed Encoder Based on Neural Network for Remote Sensing Image

Abstract:

The characteristics of remote sensing image are big data volume and high resolution. It is difficult to meet actual demands by using traditional techniques for transmission and storage. This paper studies and designs remote sensing image compression encoder with FPGA technology and three-layer feedforward BP neural network. The neural network can parallelly process large amounts of data, and has a structural characteristic of self-learning and self-organizing. The encoder has a simple structure, safety, fast speed, good reconfiguration. It overcomes shortcomings of conventional compression techniques which compress high-resolution images ineffectively. The study has some theoretical values in the field of image compression.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

4100-4103

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.4100

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Online since:

July 2014

Authors:

Ling Tao*, Wen Long Li, Yan Yan Yu, Jian Kang, Jun Yang

Keywords:

Compression Encoder, FPGA, Neural Network (NN), Remote Sensing Image

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* - Corresponding Author

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