Abstract

Abstract. Over the last decade, the French space agency (CNES) has designed and successfully operated high-resolution satellites such as Pléiades. High-resolution satellites typically acquire panchromatic images with fine spatial resolutions and multispectral images with coarser samplings for downlink constraints. The multispectral image is reconstructed on the ground, using pan-sharpening techniques. Onboard compression and ground processing affect however the quality of the final product. In this paper, we describe our next-generation onboard/on-ground image processing chain for high-resolution satellites. This paper focuses on onboard compression, compression artefacts correction, denoising, deconvolution and pan-sharpening. In the first part, we detail our fixed-quality compression approach, which limits compression effects to a fraction of the noise, thus preserving the useful information in an image. This approach optimises the bitrate at the cost of image size, which depends on the scene complexity. This technique requires however pre- and post-processing steps. The noisy HR images obtained after decompression are suited for non-local denoising algorithms. We show in the second part of this paper that non-local denoising outperforms previous techniques by 15% in terms of root mean-squared error when tested on simulated noiseless references. Deconvolution is also detailed. In the final part of this paper, we put forward an adaptation of this chain to low-cost CMOS Bayer colour matrices. We demonstrate that the concept of our image chain remains valid, provided slight modifications (in particular dedicated transformations of the colour planes and demosaicing). A similar chain is under investigation for future missions.

Highlights

  • The use of high resolution (HR) remote sensing images has significantly increased in the past years either for civilian, scientific and military uses

  • The growing demand for high resolution satellite images requires to continuously improve both the hardware and software related to image processing

  • We have presented our next-generation onground/in-flight imaging chain for high resolution satellite applications

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Summary

INTRODUCTION

The use of high resolution (HR) remote sensing images has significantly increased in the past years either for civilian (urbanism, disaster management, meteorology), scientific (hydrology, bathymetry, vegetation oversight) and military uses. Space agencies and private companies have launched Earth observation satellites providing high-resolution images to cover this demand. Despite efforts to reduce the total size, image demand requires thousands of scenes to be acquired daily, closing the door to a lossless downlink of images This feature drives the recent improvements in data compression algorithms (Zhou et al, 2015). It adapts compression thresholds locally to maintain the same level of information over the scenes, while obtaining an average compression rate similar to previous fixed bitrate techniques This approach creates a variable size of compressed images and requires additional pre- and post-processing steps. A noise restoration step is needed before denoising to recover the original noise distribution, followed by denoising, deconvolution and pan-sharpening if needed Examples of this restoration chain are provided. Examples for Bayer CFAs (Bayer, 1975) are provided

Fixed-bitrate and fixed-quality compression
Detector noise and variance stabilising transforms
Fixed-quality compression implementation
RADIOMETRIC PROCESSING CHAIN
Noise restoration and denoising
Noise restoration
Denoising
Deconvolution
Pan-sharpening
EXTENSION TO MATRIX-BASED DETECTORS
Spectral decorrelating transforms
Demosaicing and recovery of colour planes
First results on Bayer detectors
Findings
CONCLUSION
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