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The Combination of Capon Inversion Algorithm with Backscattering Statistical Models to Enhance Forest Height Estimation in a Tomographic SAR Application

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Abstract

Synthetic aperture radar tomography is a technique that can be used to retrieve the digital surface model of a studied area. It is based on the acquisition of N images of the same scene obtained at different viewing angles to retrieve backscattering profile. Many standard inversion techniques exist in the literature, but in this paper, we mainly focus on increasing the performances of Capon inversion. This algorithm is a non parametric estimation method, it is able to retrieve the volume height of forested area. Although, Capon inversion gives acceptable results, it is possible through using backscattering statistical models to increase the performances of Capon algorithm. In this paper, we present new hybrid algorithms, based on the combination of Capon inversion with statistical models of the reflectivity signal. We selected three models for the volume height, uniform distribution, exponential distribution, and Gaussian distribution, and for each probability distribution, we have computed analytically the corresponding hybrid inversion. To validate the proposed approach, we used the BioSAR 2008 dataset obtained in a boreal forest situated in northern Sweden. Results of the proposed algorithms are validated quantitatively by measuring the detection rate of the forest height according to the relative error for the whole area. Qualitatively, by analyzing images of DSM, relative error and generated histograms. It was proven that the combination between Capon inversion and a Gaussian model yields the best detection rate of 50.4% for a relative error of 0.3. The exponential model achieved 47.6%, the uniform model 42.6% and the standard Capon 38.6%.

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Authors and Affiliations

  1. Laboratory SETRAM, Ecole Nationale Supérieure Maritime, BT.61, 42415, Bou-Ismail, Algeria

    Hichem Mahgoun & Ali Taieb

  2. Laboratory LTIR, Faculty of Electronics and Computer Science, University of Science and Technology Houari Boumediene, BP 32, 16111, EL Alia, Algiers, Algeria

    Boussad Azmedroub & Mounira Ouarzeddine

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  1. Hichem Mahgoun
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  2. Ali Taieb
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  4. Mounira Ouarzeddine
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Manuscript title : The combination of CAPON inversion algorithm with backscattering statistical models to enhance forest height estimation in a tomographic SAR application. All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each author certifies that this material or similar material has not been and will not be submitted to or published in any other publication before its appearance in the Sensing and Imaging. Authorship contributions: Conception and design of study: H. Mahgoun, A. Taieb, B. Azmedroub , M. Ouarzeddine Acquisition of data: H. Mahgoun Analysis and/or interpretation of data: H. Mahgoun, A. Taieb, B Drafting the manuscript: H. Mahgoun, A. Taieb, B. Azmedroub , M. Ouarzeddine Revising the manuscript critically for important intellectual content: H. Mahgoun, A. Taieb, B. Azmedroub, M. Ouarzeddine, Approval of the version of the manuscript to be published: H. Mahgoun, A. Taieb, B. Azmedroub , M. Ouarzeddine

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Mahgoun, H., Taieb, A., Azmedroub, B. et al. The Combination of Capon Inversion Algorithm with Backscattering Statistical Models to Enhance Forest Height Estimation in a Tomographic SAR Application. Sens Imaging 25, 5 (2024). https://doi.org/10.1007/s11220-023-00452-3

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