基于网格搜索优化CatBoost模型的GF-5卫星影像铬离子含量反演研究

doi:10.16258/j.cnki.1674-5906.2024.09.013

生态环境学报 ›› 2024, Vol. 33 ›› Issue (9): 1460-1470.DOI: 10.16258/j.cnki.1674-5906.2024.09.013

• 研究论文【环境科学】 • 上一篇下一篇

基于网格搜索优化CatBoost模型的GF-5卫星影像铬离子含量反演研究

刘东宜¹(), 屈永华¹^,²^,^*(), 冯耀伟¹, 屈冉³

1.北京师范大学/遥感科学国家重点实验室，北京 100875
2.北京市陆表遥感数据产品工程技术研究中心/北京师范大学地理科学学部遥感科学与工程研究院，北京 100875
3.生态环境部卫星环境应用中心，北京 100094

收稿日期:2024-05-13 出版日期:2024-09-18 发布日期:2024-10-18
通讯作者: ^*屈永华。E-mail: qyh@bnu.edu.cn
作者简介:刘东宜（2001年生），男，硕士研究生，研究方向为灾害遥感。E-mail: liudy@mail.bnu.edu.cn
基金资助:
国家自然科学基金重大项目课题(42192581)

Research on Chromium Ion Content Inversion of GF-5 Satellite Images Based on Grid Search Optimization CatBoost Model

LIU Dongyi¹(), QU Yonghua¹^,²^,^*(), FENG Yaowei¹, QU Ran³

1. Beijing Normal University/State Key Laboratory of Remote Sensing Science, Beijing 100875, P. R. China
2. Beijing Engineering Research Center for Global Land Remote Sensing Products/Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, P. R. China
3. Satellite Application Center for Ecology and Environment, Beijing 100094, P. R. China

Received:2024-05-13 Online:2024-09-18 Published:2024-10-18

摘要/Abstract

摘要：

高光谱遥感技术以其低成本、高效率、广泛覆盖范围、宏观性强和动态监测能力等优势而受到关注。利用高光谱遥感技术，对安图县新合乡尾矿库土壤中六价铬离子污染状况进行有效监测；以高分五号（GF-5）卫星的高光谱遥感影像及实测土壤样本为数据源，基于皮尔逊相关系数、波段加法和比值指数，提取与土壤重金属含量变化相关的特征，构建反演模型。首先，剔除预处理后的遥感影像中饱和、噪声和水汽吸收波段，同时利用各波段反射率进行代数运算构建加法和比值指数，计算其与实测铬离子含量数据的皮尔逊相关系数；然后，基于皮尔逊相关系数得到的前50最高相关特征变量，采用网格搜索优化的CatBoost回归，建立土壤六价铬离子含量反演模型，并使用SHAP方法评估特征重要性，探究影响GF-5反演土壤重金属重要波段。结果显示，在相同光谱变换条件下，与偏最小二乘回归（PLSR）、随机森林回归（RFR）、支持向量机回归（SVMR）、卷积神经网络（CNN）和多元逐步线性回归模型相比，网格搜索优化的CatBoost模型效果最好，训练集拟合优度为0.92，验证集为0.88。利用网格搜索优化CatBoost回归模型对吉林省安图铬渣填埋场进行了土壤六价铬离子含量反演，结果显示该区域尾矿库开采区域周边六价铬离子污染严重，这与该地矿山开采和尾矿堆放实际情况基本一致。该研究为土壤重金属污染监测和环境管理提供了重要的技术手段和科学依据。

关键词: 高光谱反演, 高光谱遥感, GF-5, 六价铬离子, 土壤重金属, CatBoost回归模型

Abstract:

Hyperspectral remote sensing technology has attracted attention because of its advantages of low cost, high efficiency, wide coverage, macroscopicity, and dynamic monitoring capability, which make it possible to carry out large-scale soil heavy metal inversion. Hyperspectral remote sensing technology was used to effectively monitor the contamination status of hexavalent chromium ions in the soil of a tailing pond in Xinhe Township, Antu County. Hyperspectral remote sensing images from the Gaofen-5 (GF-5) satellite and measured soil samples were used as data sources. Based on the Pearson's correlation coefficient, band addition, and ratio index, features related to changes in the heavy metal content of the soil were extracted to construct an inversion model. First, the saturated, noise and moisture absorption bands in the pre-processed remote sensing images were excluded, and the addition and ratio indices were constructed algebraically using the reflectance of each band to calculate the Pearson correlation coefficients with the measured chromium ion content data. Then, based on the Pearson's correlation coefficient of the top 50 most relevant feature variables, the CatBoost regression optimized by grid search was used to build an inversion model of soil hexavalent chromium ion content, and the SHAP method was used to evaluate the importance of the features and explore the important bands affecting the inversion of soil heavy metals by GF-5. The results showed that under the same spectral transformation conditions, the grid search optimized CatBoost model was the most effective with a goodness of fit of 0.92 for the training set and 0.88 for the validation set compared with partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVMR), convolutional neural network (CNN), and multivariate stepwise linear regression models. The results were obtained with a goodness of fit of 0.92 and a goodness of fit of 0.88 for the training set and the validation set respectively. The search optimized CatBoost regression model was used to invert the soil hexavalent chromium ion content of the chromium slag landfill site in Antu, Jilin Province, and the results showed that hexavalent chromium ion contamination around the tailings pond mining area in the region was serious, which is consistent with the actual situation of mining and tailings dumping in the area. This study provides an important technical and scientific basis for monitoring soil heavy metal pollution and environmental management.

Key words: hyperspectral inversion, hyperspectral remote sensing, GF-5, hexavalent chromium ions, soil heavy metals, CatBoost regression model

中图分类号:

TP7
X833

刘东宜, 屈永华, 冯耀伟, 屈冉. 基于网格搜索优化CatBoost模型的GF-5卫星影像铬离子含量反演研究[J]. 生态环境学报, 2024, 33(9): 1460-1470.

LIU Dongyi, QU Yonghua, FENG Yaowei, QU Ran. Research on Chromium Ion Content Inversion of GF-5 Satellite Images Based on Grid Search Optimization CatBoost Model[J]. Ecology and Environment, 2024, 33(9): 1460-1470.

图/表 10

图1 研究区地理位置和野外实测土壤样本分布图基于自然资源部标准地图服务网站 2023 年发布的审图号为S（2020）179号的标准地图制作，底图边界无修改

Figure 1 Geographical location and field measured soil sample distribution map of the study area

图2 研究区饱和像元分布情况与GF-5样本点原始DN值图像

Figure 2 Distribution of saturated pixels in the study area and Raw DN value image of GF-5 sample points

图3 六价铬离子浓度反演流程图

Figure 3 Flow Chart for inversion of hexavalent chromium ion concentration

图4 单波段皮尔逊相关系数

Figure 4 Pearson correlation coefficient obtained from single band calculation

图5 构建波段指数后的皮尔逊相关系数

Figure 5 Pearson correlation coefficient obtained from band index calculation

图6 土壤六价铬离子含量反演散点图

Figure 6 Scatter plot of soil hexavalent chromium ion concentration inversion

表1 拟合效果参数对比表

Table 1 Comparison Table of Fitting Effect Parameters

回归模型	训练集		验证集
回归模型	R²	σ_RMSE	R²	σ_RMSE
随机森林回归	0.74	155.05	0.8	35.94
支持向量机回归	0.92	83.77	−0.89	109.73
偏最小二乘回归	0.32	250.73	−0.79	106.71
卷积神经网络	0.07	193.62	0.5	56.58
多元逐步回归	0.24	265.26	−4.65	189.54
CatBoost 回归	0.92	87.73	0.88	27.94

图7 CatBoost波段重要性排序

Figure 7 Ranking of band importance with method of CatBoost

图8 SHAP特征重要性评价图

Figure 8 Feature importance evaluation of SHAP

图9 研究区遥感影像CatBoost反演结果图

Figure 9 CatBoost inversion results of remote sensing images in the study area

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基于网格搜索优化CatBoost模型的GF-5卫星影像铬离子含量反演研究

Research on Chromium Ion Content Inversion of GF-5 Satellite Images Based on Grid Search Optimization CatBoost Model

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