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

Yupeng Kang,Youfeng Zou,Qingyan Meng,Miao Liu,Xuemiao Wang

doi:10.3390/s21134328

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.

Highlights

Crop classification is an important part of agricultural remote sensing monitoring, as well as the basis and key link of the application of remote sensing technology in the field of agriculture [1]
Combined with the results of the adaptive band selection (ABS) index analysis, it can be concluded that the near-infrared band (NIR) and the two red edge bands (RE1, Red edge 2 (RE2)) have more information content than the visible light bands (R, G, B), and the order of information content is as follows: NIR > RE2 > Red edge 1 (RE1) > R > G > B
The results show that the near-infrared and red edge bands of GF-6 WFV data can provide more information, which is beneficial to the classification of crops and other ground features

Summary

Introduction

Crop classification is an important part of agricultural remote sensing monitoring, as well as the basis and key link of the application of remote sensing technology in the field of agriculture [1]. With the continuous development of remote sensing science and technology, moderateto-high spatial resolution remote sensing data have been widely used in land use and land cover classification, crop classification, and so on [6,7,8,9]. The traditional moderate-to-high spatial resolution multispectral optical satellite payload is mainly composed of four bands: blue (450–520 nm), green (520–590 nm), red (630–690 nm) and near-infrared (770–890 nm). Red edge information was first used in hyperspectral remote sensing, which is often used in the inversion of vegetation physiological and biochemical parameters such as chlorophyll content, leaf area index (LAI), biomass, nitrogen content, crop growth and pest monitoring, etc. Some studies have shown that the red edge band can enhance the separability between different ground features, which plays an important role in improving the accuracy of crop remote sensing classification [15,16]

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