OLI与HSI影像融合的土壤盐分反演模型  

Soil salinity retrieval model based on OLI and HSI image fusion

作　　者：厉彦玲[1,2] 赵庚星[2] 常春艳 王卓然[2] 王凌[2] 郑佳荣 

机构地区：[1]山东农业大学信息科学与工程学院,泰安271018 [2]山东农业大学资源与环境学院,泰安271018 [3]北京工业技术学院,北京100042

出　　处：《农业工程学报》2017年第21期173-180,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金："十二五"国家科技支撑计划项目课题(2013BAD05B06;2015BAD23B0202);国家自然科学基金(41271235);"双一流"奖补资金(SYL2017XTTD02)

摘　　要：土壤盐渍化问题是黄河三角洲地区主要的土地退化问题,借助遥感技术快速、准确地掌握土壤盐渍化信息,对农业可持续发展具有重要意义。该文以黄河三角洲垦利县为研究区,利用超球体色彩空间变换算法,将环境一号卫星HSI高光谱影像与Landsat 8 OLI多光谱影像进行融合,选择土壤盐分的特征波段,结合土壤盐分的实测数据,建立统计分析模型(多元线性回归、偏最小二乘回归)和机器学习模型(BP神经网络、支持向量机和随机森林),对土壤盐分进行遥感反演。结果表明:OLI影像的统计分析模型和机器学习模型精度均较低,精度最高的随机森林模型相关系数仅为0.570;HSI影像的反演模型精度高于OLI,BP神经网络模型相关系数为0.607;融合影像反演模型精度明显高于HSI影像和OLI影像,土壤盐分含量的实测值与机器学习模型预测值具有良好的相关性,BP神经网络模型、支持向量机模型和随机森林模型的决定系数R^2分别达到0.966、0.821和0.926,模型反演精度较高。研究表明,多光谱和高光谱影像融合能显著提高土壤盐分遥感反演精度,机器学习模型的反演效果明显优于统计分析模型。研究结果对黄河三角洲典型地区的土壤盐分反演具有积极的理论和实践意义。Soil salinization is the main problem of land degradation in the Yellow River Delta of China. Remote sensing technology can gain soil salinization information quickly and accurately, which is of great significance to the sustainable development of agriculture. In this paper, a typical salinization area in Kenli County of the Yellow River Delta was chosen as the study area. In order to retrieve soil salinity from hyperspectral imagery with high accuracy, image fusion and machine learning were used in this study. HSI （hyperspectral imaging radiometer） hyperspectral imagery of HJ-1A satellite of China and OLI （operational oand imager） multispectral imagery of Landsat 8 of USA （United States of America） were preprocessed, including radiometric calibration, atmospheric correction and image registration. After that, the 2 kinds of images were fused with the hyperspherical color space resolution merge algorithm. This algorithm was designed for 8-band data of Worldview-2 sensor, and it works with any multispectral data containing 3 bands or more. The fused image has 30 m spatial resolution and 4.32 nm spectral resolution, in which saline soil can be identified better than that in the original image. The feature bands were selected according to spectral analysis of different levels of saline soil and the PLSR （partial least squares regression） regression coefficients between soil salinity and imagery bands. Two types of models, i.e. statistical model and machine learning model, were built. The statistical model includes multi linear regression model and PLSR model, while the machine learning model includes BP （back propagation） neural network model, support vector machine （SVM） model and random forest （RF） model. These models were built with soil salinity data as retrieval target and feature bands of images as input variables. In this process, natural logarithm function was adopted for soil salinity data to obey the normal distribution. The research gained the following results. Firstly, the re

关 键 词：土壤 盐分 遥感 模型 影像融合 机器学习 

分 类 号：S12[农业科学—农业基础科学]