基于高光谱图像和新特征选择算法预测土壤碳氮含量

摘要：实时快速检测土壤碳氮含量对促进生态系统碳平衡、保证农作物健康生长、土地可持续利用、土壤污染预测预警 具有重要意义。高光谱图像是预测土壤碳氮元素的一种很有前途的替代方法。考虑到高光谱图像中的一些噪声和干扰信 息，提出了一种新的特征选择算法——扩展连续投影算法（ESPA）。与使用全光谱、连续投影算法（SPA）、无信息变量 消除（UVE）、遗传算法（GA）和手动选择特征光谱的预测相比，使用 ESPA 选择光谱区域可以提高预测效果。结果表 明，使用ESPA选择的光谱区域对土壤总碳（（ TC）和总氮（（ TN）含量的预测是有效的，为利用高光谱图像预测土壤中碳 和氮含量提供了一种替代方法。 索引词：高光谱图像、土壤、碳、氮、特征选择 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

DOI：10.1109/WHISPERS52202.2021.9483965

设备：GaiaField

Prediction of Soil Carbon and Nitrogen Content Using Hyperspectral Image with A New Feature Selection Algorithm

Abstract：The real-time and rapid detection of soil carbon and nitrogen content is of great significance to promote ecosystem carbon balance, ensure the healthy growth of crops, sustainable land use, prediction and early warning of soil pollution. Hyperspectral image is a promising alternative to predict soil carbon and nitrogen elements. Considering some noise and interference information in hyperspectral image, a new feature selection algorithm, extend successive projections algorithm (ESPA), was proposed. Compared with the prediction using full spectrum, successive projection algorithm (SPA), uninformative variable elimination (UVE), genetic algorithm (GA) and manual selection of feature spectra, the prediction was improved by using ESPA selecting the spectral region. The result indicates that the selected spectral region using ESPA is effective in prediction of soil total carbon (TC) and total nitrogen (TN) content, providing an alternative to predict carbon and nitrogen content in soil using hyperspectral image.

Keywords：Hyperspectral image, soil, carbon, nitrogen, feature selection