Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data

• URL: http://arxiv.org/abs/2607.03663v3
• Date: Thu, 09 Jul 2026 15:14:29 GMT
• Title: Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data
• Abstract summary: Trimodal Coherent Co-attention Transformer (TCCT) is a physics-informed deep learning architecture.<n>TCCT fuses optical surface reflectance (Landsat-5) with complex-valued Polarimetric SAR Interferometry (PolInSAR) data from both P and L bands.<n>When converted to Above-Ground Biomass (AGB), the fine-tuned TCCT map yielded a Relative RMSE of 4.51% in dense forest areas above 50 Mg/ha.
• Score: 0.3499870393443268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The accurate estimation of Above-Ground Biomass (AGB) in mature tropical forests remains a critical challenge in remote sensing, primarily due to the saturation of Synthetic Aperture Radar (SAR) signals in high-density areas and persistent cloud cover affecting optical imagery. To overcome these physical limitations, we propose the Trimodal Coherent Co-attention Transformer (TCCT), a physics-informed deep learning architecture. The TCCT natively fuses optical surface reflectance (Landsat-5) with complex-valued Polarimetric SAR Interferometry (PolInSAR) data from both P and L bands. Unlike traditional fusion methods, our architecture employs complex-valued encoders to preserve spatial phase coherence, coupled with a dynamic co-attention mechanism that acts as an adaptive gating module, reducing the weight of cloud-corrupted optical pixels and shifting reliance to microwave phase data. We also derived a localized spatial allometric calibration model via Levenberg-Marquardt optimization, tailored to the specific wood density of the Paracou region in the Amazon basin. Evaluated using a two-stage protocol, the TCCT first underwent a rigorous 5-fold cross-validation to establish robust global weights (achieving a global RMSE of 4.19 m). Subsequently, following a localized spatial fine-tuning phase over 200 epochs, the model attained an absolute RMSE of 3.78 m and an $R^2$ of 0.33 for Canopy Height Models (CHM), outperforming standard Random Forest, CNN, and Vision Transformer baselines. Our ablation study confirms that preserving phase coherence mitigates deep-canopy signal saturation. When converted to AGB, the fine-tuned TCCT map yielded a Relative RMSE (rRMSE) of 4.51% in dense forest areas above 50 Mg/ha. By meeting the European Space Agency (ESA) BIOMASS mission requirement of less than 20% error, the TCCT provides a robust framework for continuous carbon stock mapping in tropical biomes.

Related papers

• Computational Depth Measurement in Thermographic Video: Overcoming Spatial Overfitting via Spatio-Temporal Decoupling [2.5621346794752724]
  Optical pulsed thermography (OPTOPT) provides a two-dimensional thermal video rather than volumetric measurements.<n>This work introduces architecture that separates spatial defect localization from temporal depth measurement.<n>Four regression models are evaluated using specimen-level cross-validation.
  arXiv  Detail & Related papers  (2026-08-29T12:26:27Z)
• Rethinking Attention Locality in Spiking Transformers [50.00501275379363]
  Spiking Transformers provide a promising paradigm for efficient visual processing with spike-driven computation.<n>Their Softmax-free Spiking Self-Attention (SSA) struggles to establish spatially localized token interactions.<n>We propose Spatially Contiguous Local Attention with Boundary Continuity Pathway (SCLA-BCP)<n>Our approach improves mAP@50 by up to 9.50% on COCO on 2017 and mIoU by up to 3.42% on ADE20K.
  arXiv  Detail & Related papers  (2026-08-09T07:36:17Z)
• Air Quality Downscaling with Station-Guided Pseudo-Supervision [60.31045749983062]
  Super-resolving coarse atmospheric fields to local PM$_2.5$ variations is uniquely challenged by a mismatch in spatial support.<n>We present a station-guided framework for high-resolution PM$_2.5$ downscaling over Europe.
  arXiv  Detail & Related papers  (2026-07-06T16:32:25Z)
• Multi-Modal Conditioned High-Resolution Transformer for Urban Electromagnetic Field Map Prediction Download PDF [3.3172899945646965]
  Predicting electromagnetic field (EMF) strength in urban environments is essential for cellular network planning.<n>We propose a multi-conditioned dense prediction framework that generates 500 500 EMF maps from building layout images and antenna configurations.<n>Our best model achieves a test MAE of 0.0461, a 25.2% improvement over a plain UNet baseline and 31.8% over an HRFormer-only baseline.
  arXiv  Detail & Related papers  (2026-06-26T03:06:23Z)
• High-Dimensional Latents Should Be Diagnosed Through Phase Structure [56.362776482614976]
  We study autoencoder and variational-autoencoder latent spaces through the lens of spin-glass theory.<n>We formalize a latent-space spin-glass dictionary for a fixed decoder.<n>We show that driving the latent system toward the edge-of-stability of the topological trivialization regime has concrete downstream consequences.
  arXiv  Detail & Related papers  (2026-05-23T06:17:23Z)
• Sub-metre Lunar DEM Generation and Validation from Chandrayaan-2 OHRC Multi-View Imagery Using an Open-Source Pipeline [0.2174820084855635]
  High-resolution digital elevation models (DEMs) of the lunar surface are essential for surface mobility planning, landing site characterization, and planetary science.<n>This work presents, for the first time, the generation of sub-metre DEMs from Orbiter High Resolution Camera (OHRC) multi-view imagery using an exclusively open-source pipeline.<n>Dense stereo correspondence and ray triangulation are then applied to generate point clouds, which are gridded into DEMs at effective spatial resolutions between approximately 24 and 54 cm across five geographically distributed lunar sites.
  arXiv  Detail & Related papers  (2026-04-01T15:43:04Z)
• Atmos-Bench: 3D Atmospheric Structures for Climate Insight [0.0]
  We present Atmos-Bench: the first 3D atmospheric benchmark, along with a novel FourCastX: Frequency-enhanced Spatio-Temporal Mixture-of-Experts Network that generates 921,600 image slices from 3D scattering volumes simulated at 532 nm and 355 nm by coupling WRF with an enhanced COSP simulator over 384 land-ocean time steps, yielding high-quality voxel-wise references; (b) embeds ATB-BC physical constraints into the model architecture, promoting energy consistency during restoration; (c) achieves consistent improvements on the Atmos-Bench dataset across both 355 nm and 532 nm bands,
  arXiv  Detail & Related papers  (2025-07-15T08:27:29Z)
• ReconMOST: Multi-Layer Sea Temperature Reconstruction with Observations-Guided Diffusion [48.540756751934836]
  ReconMOST is a data-driven guided diffusion model framework for multi-layer sea temperature reconstruction.<n>Our method extends ML-based SST reconstruction to a global, multi-layer setting, handling over 92.5% missing data.
  arXiv  Detail & Related papers  (2025-06-12T06:27:22Z)
• Estimating the Diameter at Breast Height of Trees in a Forest With a Single 360 Camera [52.85399274741336]
  Forest inventories rely on accurate measurements of the diameter at breast height (DBH) for ecological monitoring, resource management, and carbon accounting.<n>While LiDAR-based techniques can achieve centimeter-level precision, they are cost-prohibitive and operationally complex.<n>We present a low-cost alternative that only needs a consumer-grade 360 video camera.
  arXiv  Detail & Related papers  (2025-05-06T01:09:07Z)
• Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights [50.52704854147297]
  We present a new vision transformer (ViT) model optimized with a classification (discrete) and a continuous loss function. This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
  arXiv  Detail & Related papers  (2023-04-22T22:39:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.