Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data

Abstract

A red edge band is a sensitive spectral band of crops, which helps to improve the accuracy of crop classification. In view of the characteristics of GF-6 WFV data with multiple red edge bands, this paper took Hengshui City, Hebei Province, China, as the study area to carry out red edge feature analysis and crop classification, and analyzed the influence of different red edge features on crop classification. On the basis of GF-6 WFV red edge band spectral analysis, different red edge feature extraction and red edge indices feature importance evaluation, 12 classification schemes were designed based on GF-6 WFV of four bands (only including red, green, blue and near-infrared bands), stepwise discriminant analysis (SDA) and random forest (RF) method were used for feature selection and importance evaluation, and RF classification algorithm was used for crop classification. The results show the following: (1) The red edge 750 band of GF-6 WFV data contains more information content than the red edge 710 band. Compared with the red edge 750 band, the red edge 710 band is more conducive to improving the separability between different crops, which can improve the classification accuracy; (2) According to the classification results of different red edge indices, compared with the SDA method, the RF method is more accurate in the feature importance evaluation; (3) Red edge spectral features, red edge texture features and red edge indices can improve the accuracy of crop classification in different degrees, and the red edge features based on red edge 710 band can improve the accuracy of crop classification more effectively. This study improves the accuracy of remote sensing classification of crops, and can provide reference for the application of GF-6 WFV data and its red edge bands in agricultural remote sensing.

Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

“…The wavelength of the red edge is between those of Red and NIR, and the waveband range is approximately 670-780 nm, which is an area in which the spectral albedo of green vegetation rises rapidly within a certain waveband range; it is a sensitive spectral waveband for vegetation that is closely related to the pigment status and physical and chemical properties of crops and other vegetation. Some studies have also shown that the red edge can enhance the separability of different vegetation types and play an important role in increasing the accuracy of remote-sensing classification for crops [67]. According to the research results, Red Edge1 was more effective at extracting the physiological characteristics of vegetation than the other two red-edge bands.…”

Recognition of spider mite infestations in jujube trees based on spectral-spatial clustering of hyperspectral images from UAVs

“…Traditional band selection used the standard deviation and correlation coefficient to construct the optimum index factor to select the optimum band combination based on the information content of each band, but the computational effort required to calculate the correlation coefficient among the bands of hyperspectral data is too large for this method to be used for hyperspectral band selection. Then adaptive band selection method was proposed, which calculates the ratio of the standard deviation of each band to the correlation coefficient of the adjacent bands as an index to select the best band, which reduces the computational effort ( Baisantry et al., 2021 ; Kang et al., 2021b ). However, the adaptive band selection method does not consider the connection between the candidate bands and the non-adjacent bands and therefore the selected band combination may not be the optimum solution ( Yang and Kan, 2019 ).…”

The Impact of NPV on the Spectral Parameters in the Yellow-Edge, Red-Edge and NIR Shoulder Wavelength Regions in Grasslands

“…Red-edge parameters, including red-edge position (REP), red-edge amplitude (REA) and red-edge area [26,27], are the most popular indices for detecting the biophysical characteristics of vegetation growth, including chlorophyll content [27], LAI [24], biomass [28], nitrogen status [29], growth stage [30,31] and vegetation response to stress and disturbance [27,32,33]. Red-edge parameters also enhance the separation among vegetation types [28,34] and different ground features [28]. The satellite (RapidEye, WorldView-2/3, Sentinel-2 and Gaofen-6) imagery, designed with red-edge bands, is helpful to improve classification accuracy and enhance LAI estimation by migrating saturation issues [28,[35][36][37][38].…”

“…Red-edge parameters also enhance the separation among vegetation types [28,34] and different ground features [28]. The satellite (RapidEye, WorldView-2/3, Sentinel-2 and Gaofen-6) imagery, designed with red-edge bands, is helpful to improve classification accuracy and enhance LAI estimation by migrating saturation issues [28,[35][36][37][38]. Although, comparing to vegetation indices, red-edge parameters are less sensitive to the changes in specific biophysical factors (vegetation type, canopy structure and soil cover) and environment conditions (site condition, atmospheric effects, irradiance and solar zenith angle) [29,39].…”