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About this paper
Zhenyu Yu, Mohd Yamani Idna Idris, Hua Wang, Pei Wang, Junyi Chen, Kun Wang
The review analyzes the progression of quantitative remote sensing inversion, categorizing approaches from physics-based models to data-driven methods and emerging foundation models. It offers a systematic synthesis of the methodological evolution, highlighting current progress, modeling principles, and future directions for developing robust and scalable inversion frameworks that integrate physical consistency with AI capabilities.
Key insights
- Quantitative remote sensing inversion is evolving towards a "cognition-driven modeling paradigm" that fuses physical theory with data-centric AI learning approaches.
- Integrating physical constraints and knowledge (e.g., radiative transfer models) into AI architectures is crucial for enhancing interpretability, ensuring physical plausibility, and building scientific trust in model predictions.
- Persistent challenges include limited interpretability of black-box models, poor generalization across diverse domains and sensors, and the critical dependence on scarce, high-quality labeled data.
Summary by alphaXiv.
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