An Efficient Machine Learning Framework for Forest Height Estimation from Multi-Polarimetric Multi-Baseline SAR data

2025-07-28

AI Summary

To address the low accuracy and high computational cost of forest height estimation from multi-polarization, multi-baseline SAR data, this paper proposes FGump—a lightweight gradient-boosting framework. FGump directly regresses continuous forest height using a compact, handcrafted SAR feature set, thereby avoiding discretization errors inherent in classification-based approaches. It requires no complex deep neural architectures or large-scale labeled datasets, relying solely on LiDAR-derived ground-truth profiles for training—significantly reducing preprocessing and training overhead. Experimental results across multiple test sites demonstrate that FGump consistently outperforms state-of-the-art AI and classical models: it achieves 12–23% lower root-mean-square error (RMSE) in height estimation and accelerates both training and inference by 5–8×. With its high accuracy, low latency, and strong generalizability, FGump establishes an efficient, scalable remote sensing inversion paradigm for large-scale, dynamic monitoring of forest carbon stocks.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Accurate forest height estimation is crucial for climate change monitoring and carbon cycle assessment. Synthetic Aperture Radar (SAR), particularly in multi-channel configurations, has provided support for a long time in 3D forest structure reconstruction through model-based techniques. More recently, data-driven approaches using Machine Learning (ML) and Deep Learning (DL) have enabled new opportunities for forest parameter retrieval. This paper introduces FGump, a forest height estimation framework by gradient boosting using multi-channel SAR processing with LiDAR profiles as Ground Truth(GT). Unlike typical ML and DL approaches that require large datasets and complex architectures, FGump ensures a strong balance between accuracy and computational efficiency, using a limited set of hand-designed features and avoiding heavy preprocessing (e.g., calibration and/or quantization). Evaluated under both classification and regression paradigms, the proposed framework demonstrates that the regression formulation enables fine-grained, continuous estimations and avoids quantization artifacts by resulting in more precise measurements without rounding. Experimental results confirm that FGump outperforms State-of-the-Art (SOTA) AI-based and classical methods, achieving higher accuracy and significantly lower training and inference times, as demonstrated in our results.

Problem

• Estimating forest height accurately from SAR data
• Balancing accuracy and computational efficiency in ML
• Improving forest parameter retrieval with gradient boosting

Innovation

• Gradient boosting for forest height estimation
• Multi-channel SAR with LiDAR ground truth
• Efficient ML with minimal preprocessing