Timo Balz | Searcharxiv

arXiv2026

The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation

Monocular building height estimation from optical imagery is important for characterizing urban vertical structure, yet remains challenging due to the heterogeneity of urban building morphology and the indirect relationship between optical image appearance and building height. The recently launched PhiSat-2 satellite provides a promising open-access data source for this task, with 4.75m spatial resolution and seven multispectral bands spanning the visible to near-infrared range. However, its suitability for monocular building height estimation has not been systematically assessed. This study presents an initial open-reference assessment of PhiSat-2 imagery for this task by constructing a PhiSat-2--Height Dataset (PHDataset) and proposing a Two-Stream Ordinal Network (TSONet). PHDataset integrates global PhiSat-2 imagery with open building-height references and contains 9,475 co-registered patch pairs from 26 cities worldwide. TSONet jointly learns dense height estimation and auxiliary footprint prediction, using footprint-aware structural guidance and ordinal height modeling to better exploit PhiSat-2 spatial--spectral information. Specifically, a Cross-Stream Exchange Module (CSEM) enables adaptive interaction between the height and footprint streams, while a Feature-Enhanced Bin Refinement (FEBR) module performs coarse-to-fine ordinal query refinement with multi-level features. Experiments on PHDataset show that TSONet outperforms representative competing methods, reducing MAE and RMSE by over 13.2% and 9.7%, respectively, while improving IoU and F1-score by over 14.0% and 10.1%. Additional analyses further indicate that PhiSat-2 imagery contains useful spatial--spectral cues for monocular building height estimation at an intermediate spatial resolution.

cs.CV↗

arXiv2025

Shape-to-Scale InSAR Adaptive Filtering and Phase Linking under Complex Elliptical Models

Distributed scatterers in InSAR (DS-InSAR) processing are essential for retrieving surface deformation in areas lacking strong point targets. Conventional workflows typically involve selecting statistically homogeneous pixels based on amplitude similarity, followed by phase estimation under the complex circular Gaussian model. However, amplitude statistics primarily reflect the backscattering strength of surface targets and may not sufficiently capture differences in decorrelation behavior. For example, when distinct scatterers exhibit similar backscatter strength but differ in coherence, amplitude-based selection methods may fail to differentiate them. Moreover, CCG-based phase estimators may lack robustness and suffer performance degradation under non-Rayleigh amplitude fluctuations. Centered around scale-invariant second-order statistics, we propose ``Shape-to-Scale,'' a novel DS-InSAR framework. We first identify pixels that share a common angular scattering structure (``shape statistically homogeneous pixels'') with an angular consistency adaptive filter: a parametric selection method based on the complex angular central Gaussian distribution. Then, we introduce a complex generalized Gaussian-based phase estimation approach that is robust to potential non-Rayleigh scattering. Experiments on both simulated and SAR datasets show that the proposed framework improves coherence structure clustering and enhances phase estimation robustness. This work provides a unified and physically interpretable strategy for DS-InSAR processing and offers new insights for high-resolution SAR time series analysis.

stat.AP↗