[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119260-en":3,"doc-seo-119260-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119260,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Hybrid Machine Learning - Forest Height Estimation from TanDEM-X InSAR","Hybrid modeling for remote-sensing inversion combines machine learning with physical insight to improve retrieval algorithms from TanDEM-X interferometric coherence. The proposed approach derives a vertical reflectivity profile from input descriptors covering topography and acquisition geometry via a multilayer perceptron, then links this profile to forest height through an established physical relationship. Experiments on tropical TanDEM-X acquisitions use airborne LiDAR-based reference data for validation, assessing accuracy across varying acquisition geometries while evaluating performance gains in generalization and interpretability.","Hybrid Machine Learning Forest Height Estimation  \nFrom TanDEM-X InSAR  \nIslam Mansour, Member, IEEE, Konstantinos Papathanassiou, Fellow, IEEE,  \nRonny Hänsch, Senior Member, IEEE, and Irena Hajnsek, Fellow, IEEE  \nAbstract—Combining machine learning (ML) with physical models can significantly impact retrieval algorithms designed to invert geophysical parameters from remote sensing data. Such hybrid models integrate physical knowledge with domain expertise through a joint architecture, potentially enhancing performance by increasing the efficiency and flexibility of the physical model as well as the generalization and interpretability of the ML predictions. This work introduces a hybrid model for estimating forest height using single-baseline, single-polarization TanDEM-X interferometric coherence measurements. In this model, the vertical reflectivity profile is derived as a function of input features, including topographic and acquisition geometry descriptors, using a multilayer perceptron network. This profile is then used to invert forest height by leveraging the established physical relationship connecting the vertical reflectivity profile to forest height. The developed model is applied and validated on several TanDEM-X acquisitions over tropical sites with different acquisition geometries, and its performance is assessed against reference data derived from airborne LiDAR measurements.  \nIndex Terms—Forest height, forest height estimation, forest structure, hybrid modeling, InSAR, interferometry, machine learning (ML), physical modeling, remote sensing, synthetic aperture radar, TanDEM-X, temporal decorrelation, topographic effects.  \nI. INTRODUCTION  \nACCURATE measurements of forest height are rele  \nvant for forest inventory, forest disturbance, and carbon sequestration monitoring [1], [2], [3], [4], [5] . SAR interferometry, combined with polarimetric and/or spatial baseline diversity, is today one of the established remote sensing techniques for obtaining continuous forest height estimates of significant accuracy at large spatial scales. The related approaches are in first line model-based, exploring the inherent sensitivity of interferometric measurements to the 3D distribution of scatterers within forests. Model-based forest height inversion performance is well-understood and validated  \nReceived 31 July 2024; revised 1 November 2024; accepted 14 December 2024. Date of publication 19 December 2024; date of current version 3 January 2025. This work was supported by the DeepSAR Research Project, funded by Helmholtz AI under the Helmholtz Association of German Research Centers (HGF) . (Corresponding author: Islam Mansour.)  \nIslam Mansour and Irena Hajnsek are with the Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Weßling, Germany, and also with the Chair of Earth Observation and Remote Sensing, Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland (e-mail: [islam.mansour@dlr.de](islam.mansour@dlr.de); [irena.hajnsek@dlr.de](irena.hajnsek@dlr.de)).  \nKonstantinos Papathanassiou and Ronny Hänsch are with the Microwavesand Radar Institute, German Aerospace Center (DLR), 82234 Weßling, Germany (e-mail: [kostas.papathanassiou@dlr.de](kostas.papathanassiou@dlr.de); [ronny.haensch@dlr.de](ronny.haensch@dlr.de)).  \nDigital Object Identifier 10.1109/TGRS.2024.3520387  \nacross various frequencies, from the X-to P-band, for different forest and terrain conditions [6], [7], [8], [9], [10], [11], [12] . The achieved performance critically depends on the definition of the inversion model, particularly the parameterization of the vertical reflectivity profile. While accurate and generic parameterization requires a certain number of parameters tobe described, the constraint of achieving a balanced inversion model dictates the number of parameters needed to parameterize the vertical reflectivity profile to be matched by the number of available measurements. In the absence of a sufficient ","cbCaibBbJNCofHNe","https://ap.wps.com/l/cbCaibBbJNCofHNe","pdf",6191411,1,11,"English","en",105,"# Introduction\n## ML Versus Physical Versus Hybrid Modeling","[{\"question\":\"How does the hybrid model estimate forest height from TanDEM-X InSAR measurements?\",\"answer\":\"It first predicts a vertical reflectivity profile from topographic and acquisition-geometry descriptors using a multilayer perceptron, then inverts forest height using the physical relationship between the predicted profile and forest height.\"},{\"question\":\"What data are used for training, application, and validation?\",\"answer\":\"The model is applied to several TanDEM-X acquisitions over tropical sites, with performance validated against reference data derived from airborne LiDAR measurements.\"},{\"question\":\"Why combine machine learning with physical modeling in this inversion task?\",\"answer\":\"The hybrid design increases flexibility and efficiency of the physical model while improving generalization and interpretability compared with purely data-driven approaches, especially in underdetermined problems.\"}]","Hybrid Machine Learning - 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