PROSAIL-Net Inverts Vegetation Leaf Parameters via Physical Mechanism
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Solution Overview
Problem
Conventional deep learning methods for inverting parameters of vegetation leaves lack accuracy and interpretability due to the absence of physical mechanisms, making it difficult to improve the inversion results.
Innovation Solution
A deep neural network structure is developed based on the physical mechanisms of the PROSAIL model, combining the SAIL and PROSPECT models to invert reflectivity and transmittance, and subsequently obtain vegetation leaf parameters, using sub-networks like SAIL-Net and PROSPECT-Net for accurate and interpretable results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional deep learning methods are used for inverting vegetation leaf parameters, then the method can be applied widely, but the accuracy and interpretability of the inversion results deteriorate due to the absence of physical mechanisms
Solution Approach 1:
The patent introduces physical models (PROSPECT and SAIL models) as intermediaries between the deep learning network and the parameter inversion process. These models provide the physical mechanism that guides the network's learning, ensuring that the inversion results are both accurate and interpretable while maintaining wide applicability across different vegetation types and conditions
2Adaptability or versatility
If conventional deep learning methods are used for inverting vegetation leaf parameters, then the method can be applied widely, but the interpretability of the inversion results deteriorates due to the absence of physical mechanisms
Solution Approach 1:
Physical models serve as intermediaries that preserve interpretability by providing a physically meaningful framework. The network learns within the constraints of these models, ensuring that the inversion process remains interpretable while maintaining broad applicability through the universal nature of the physical laws embodied in the models
3Extent of automation
If a deep neural network model performs continuous learning and training, then parameter inversion can be realized, but the accuracy of inverting vegetation leaf parameters deteriorates without physical mechanism guidance
Solution Approach 1:
The patent changes the parameters and constraints of the deep learning model by incorporating physical model equations as constraints. This guides the automatic learning process to respect physical laws, thereby improving the accuracy of parameter inversion while maintaining automation. The network adjusts its parameters within the physically valid space defined by the PROSPECT and SAIL models
Data Source
AI summary
An apparatus for inverting parameters of vegetation leaves based on remote sensing is provided. The apparatus is obtained by performing inverse processes of a PROSAIL model based on a deep neural network, achieving strong physical mechanism and high accuracy. A method for inverting parameters of vegetation leaves based on remote sensing is provided. In the method, the apparatus for inverting parameters of vegetation leaves based on remote sensing is used, and the parameters of the vegetation leaves are obtained by performing inversion based on remote sensing data of the vegetation leaves, achieving high reliability and high accuracy.