Abstract

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches.

Highlights

  • Remote sensing multispectral images are obtained by optical multispectral camera carried on the satellite imaging multiple contiguous narrow bands of the same objects [1, 2].In general, there are dozens of bands to a few ones in the wavelength range from visible to near infrared spectrum, and the spectral resolution of multispectral is 0.1λ, such as multispectral images produced PLEIADES satellite, IKONOS satellite, and QuickBird satellite

  • We propose an effective multispectral image compression method based on distributed source coding (DSC) combined with CCSDSIDC by deep coupling way

  • In order to verify the feasibility and evaluate the proposed DSC combined with the CCSDS-image data compression (IDC)- (DSC-CCSDS-) based compression algorithm performance for multispectral images, we use several group multispectral remote sensing images

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Summary

Introduction

There are dozens of bands to a few ones in the wavelength range from visible to near infrared spectrum, and the spectral resolution of multispectral is 0.1λ, such as multispectral images produced PLEIADES satellite, IKONOS satellite, and QuickBird satellite. They can obtain abundant spatial and spectral information of measured objects simultaneously. Multispectral imaging technique has been widely applied in many fields, like science research, airborne and airspace remote sensing, medical devices, environment monitoring, geological survey, agricultural monitoring, military applications, and so on [3, 4] Another efficient method for collecting images of an object in a series of spectral windows is hyperspectral imaging. We mainly research how to compress multispectral images using an algorithm having low complexity, high robust, and high performance according to characteristics of multispectral images

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