Improving the quality of remote sensing images using a universal reconstruction method

Huanfeng Shen; Tinghua Ai; Pingxiang Li; Yi Wang

doi:10.1117/12.816609

29 December 2008 Improving the quality of remote sensing images using a universal reconstruction method

Huanfeng Shen, Tinghua Ai, Pingxiang Li, Yi Wang

Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72851G (2008) https://doi.org/10.1117/12.816609
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China

Abstract

This paper presents a universal maximum a posteriori (MAP) based reconstruction method which can be used for destriping, inpainting (the removal of dead pixels) and super resolution reconstruction (the recovery of a high resolution image from several low resolution images). In the MAP framework, the likelihood probability density function (PDF) is constructed based on a linear image observation model, and a robust Huber-Markov model is used as the prior PDF. A gradient descent optimization method is employed to produce the desired image. The proposed algorithm has been tested using MODIS images for destriping and super resolution reconstruction, and CBERS (China-Brazil Earth Resource Satellite) and QuickBird images for simulated inpainting. The experiment results and quantitative analyses verify the efficacy of this algorithm.

Citation Download Citation

Huanfeng Shen, Tinghua Ai, Pingxiang Li, and Yi Wang "Improving the quality of remote sensing images using a universal reconstruction method", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72851G (29 December 2008); https://doi.org/10.1117/12.816609

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KEYWORDS

MODIS

Reconstruction algorithms

Super resolution

Remote sensing

Image resolution

Lawrencium

Earth observing sensors

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