A Method for Discriminating Yin and Yang Leaves in Vegetation Canopy Based on Hyperspectral Imagery

Abstract

The invention discloses a method for distinguishing yin and yang leaves of a vegetation canopy based on a hyperspectral image, which includes: filtering the remote sensing image to reduce signal noise; using the depth H of the absorption valley, and the constructed red edge vegetation identification index REVI and ratio vegetation index SRVI , using the decision tree classification method to calculate the remote sensing image of the target vegetation information; constructing the identification index of yin and yang leaves in the hyperspectral vegetation canopy; using the threshold classification method to extract the spatial distribution information of the yin and yang leaves in the vegetation canopy. In the present invention, by constructing the red-edge vegetation identification index REVI and the ratio vegetation index SRVI, the decision tree method is used to eliminate the influence of the complex background on the vegetation information layer by layer, and the discrimination accuracy is high. The constructed vegetation canopy yin and yang leaf identification index can quickly and accurately identify the spatial distribution of vegetation shady and sunny leaves, and the operation process is simple. Compared with traditional digital imaging technology, the hyperspectral imaging data used in the present invention is beneficial to the non-destructive estimation of vegetation ecological parameters, can be applied to different vegetation types, and has strong universality.

Description

technical field [0001] The invention relates to a hyperspectral image-based method for discriminating yin and yang leaves in a vegetation canopy, belonging to the field of vegetation ecological remote sensing. Background technique [0002] In the natural state, vegetation is susceptible to sunlight shading due to the influence of the crop itself and the surrounding environment. Therefore, under different light receiving conditions, shady leaves and sun leaves often appear in the vegetation canopy, and the same leaf may contain both shady leaves and sun leaves. The shade and sun leaves of vegetation canopy are important basic parameters in the fields of crop growth monitoring, physiological and biochemical parameter inversion, photosynthetic characteristics and flux estimation, and material exchange between vegetation and the atmosphere. [0003] Vegetation canopy will lead to differences in plant structure, morphological characteristics, and physiological and biochemical pa...

AI Technical Summary

Problems solved by technology

Although on-site digital imaging has high precision, the measurement range is small, time-consuming and labor-intensive, and the natural growth state of the canopy will be destroyed during field sampling; in recent years, the widely used hyperspectral image data has both wide imaging range and spatial resolution, Features of high spectral resolution

At present, the hyperspectral imaging data for identification of yin and yang leaves of vegetation mainly include visible light-near-infrared and fluorescence band data sources, but fluorescence remote sensing is mainly used for the extraction of fluorescence information, which has strict requirements on equipment signal-to-noise ratio and spectral resolution, and high cost. Poor universality; and some studies using visible-near-infrared hyperspectral data did not consider the impact of complex backgrounds on vegetation extraction accuracy

Images