A Tavi Adjustment Factor Algorithm Based on Sun Altitude Angle

Abstract

The invention relates to a TAVI regulatory factor algorithm based on solar altitude. The method includes the following steps: conducting image pre-processing, analyzing the numerical distribution of image wave section apparent reflectance, extracting the solar altitude and computing a regulatory factor. According to the invention, the method obviates the need for DEM data and remote sensing image classification, independent from ground survey data, uses solar altitude in actual physics significance as a computing parameter, ensures that TAVI can effectively eliminate interference on vegetation information by orographic influence, can achieve the effects of orographic correction of a C model and even produces better effects, prevents the inversion of surface feature vegetation from being reduced in precision due to the difference between remote sensing images and DEM data registration accuracy, and provides scientific and economic basis for the large-scale application and promotion of the TAVI to the inversion of vegetation information of complex geography and mountains.

Description

technical field [0001] The invention relates to the field of TAVI adjustment factor algorithms, in particular to a TAVI adjustment factor algorithm based on the sun altitude angle. Background technique [0002] There are two main optimization methods for the adjustment factor f(Δ) in the Terrain-Adjusted Vegetation Index (TAVI) that have been published: "optimal matching method" and "extreme value optimization method". [0003] The calculation steps of the "optimal matching" algorithm (National Patent No. 200910111688X) are: (1) image classification, dividing the shady and sunny slopes of the mountain in remote sensing images, and selecting typical sample areas; (2) target recognition, using ground survey data , field survey data, aerial photography data, or high-resolution image data from GoogleEarth to verify the homogeneity of the vegetation on the shady and sunny slopes, and to identify the parts of the typical sample area where the vegetation on the shady and sunny slop...

AI Technical Summary

Problems solved by technology

However, these two optimization algorithms are empirical and weak in physical meaning, and both need to classify remote sensing images; among them, the "optimization and matching" algorithm also needs the support of ground data, while the "extreme value optimization" algorithm is easy to fall into local Optimal rather than global optimal, which limits the automation application level of TAVI, which is not conducive to the large-scale promotion and application of TAVI

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