面向土地覆盖精准分类的遥感特征参数优选方法

doi:10.11947/j.AGCS.2024.20230327

测绘学报 ›› 2024, Vol. 53 ›› Issue (7): 1401-1416.doi: 10.11947/j.AGCS.2024.20230327

面向土地覆盖精准分类的遥感特征参数优选方法

陈超¹(), 梁锦涛²^,³, 杨刚⁴(), 孙伟伟⁴, 龚绍军³, 王建强⁵

^1.苏州科技大学地理科学与测绘工程学院，江苏　苏州　215009
^2.中国地质大学（武汉）地球物理与空间信息学院，湖北　武汉　430074
^3.浙江海洋大学海洋科学与技术学院，浙江　舟山　316022
^4.宁波大学地理与空间信息技术系，浙江　宁波　315211
^5.浙江省水文地质工程地质大队，浙江　宁波　315012

收稿日期:2023-08-08 发布日期:2024-08-12
通讯作者: 杨刚 E-mail:chenchao@usts.edu.cn;yanggang@nbu.edu.cn
作者简介:陈超（1982—），男，博士，教授，研究方向为海岸带环境遥感。E-mail：chenchao@usts.edu.cn
基金资助:
国家自然科学基金(42171311);浙江省省级专项资金(2024010)

Remote sensing parameters optimization for accurate land cover classification

Chao CHEN¹(), Jintao LIANG²^,³, Gang YANG⁴(), Weiwei SUN⁴, Shaojun GONG³, Jianqiang WANG⁵

^1.School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
^2.School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
^3.Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China
^4.Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China
^5.Zhejiang Institute of Hydrogeology and Engineering Geology, Ningbo 315012, China

Received:2023-08-08 Published:2024-08-12
Contact: Gang YANG E-mail:chenchao@usts.edu.cn;yanggang@nbu.edu.cn
About author:CHEN Chao (1982—), male, PhD, professor, majors in remote sensing of coastal environment. E-mail: chenchao@usts.edu.cn
Supported by:
The National Natural Science Foundation of China(42171311);Provincial Special Fund Project of Zhejiang Province(2024010)

摘要/Abstract

摘要：

在显著气候变化叠加人类活动干扰的背景下，可持续的自然资源管理对于精准掌握土地覆盖信息的需求不断提升。为应对地表形态的复杂性、地物类型的多样性、遥感图像特征的非线性给传统遥感图像分类方法带来的挑战，本文基于随机森林Gini指数，提出了一种10%阈值决策的遥感特征参数优选方法，旨在筛选出最优的遥感特征参数组合，达到最佳土地覆盖分类效果。首先，选择光谱特征、纹理特征、温热特征、高程特征、主成分特征组成遥感影像堆栈。然后，设置多组决策树对特征贡献度进行交叉验证，并根据特征重要性的归一化均值确定特征排序。最后，设定阈值，筛选出符合要求的遥感特征参数，并迭代过程。选择覆盖江苏盐城自然保护区的Sentinel-2遥感图像开展试验，结果表明，本文方法筛选出的遥感特征参数代表性好，与CART、SVM、KNN和只使用波段信息的RF相比，分类结果地物边界清晰，类别属性准确，总体精度和Kappa系数分别为96.20%和0.955 6。本文研究能够为区域空间规划和可持续发展提供技术支持。

关键词: 土地覆盖分类, 随机森林, 特征优选, 递归特征消除, Gini指数

Abstract:

Sustainable natural resources management requires considerable accurate land cover information given the evident climate change impacts and human disturbances on wetlands. It is characterized by the convergence of numerous materials and energies, resulting in fragmented landscapes and frequent land cover changes. To address the challenges posed by the complexity of landforms, diversity of land cover types, and non-linearity of remote sensing image features in traditional remote sensing image classification methods, this paper proposes a feature parameter selection method based on the Gini index of random forests, with a 10% threshold decision. The aim is to identify the optimal combination of remote sensing feature parameters. Firstly, spectral features, texture features, thermal features, elevation features, and principal component features are selected to form a stack of remote sensing images. Then, multiple decision trees are set up to cross-validate the contributions of the features, and the feature ranking is determined based on the normalized mean importance of the features. Finally, a threshold is set to select the remote sensing feature parameters that meet the requirements, and the process is iterated. Experiments are conducted using Sentinel-2 remote sensing images covering the Yancheng Nature Reserve in Jiangsu province. The results show that the remote sensing feature parameters selected by this method have good representativeness. Compared with CART, SVM, KNN, and RF methods that only use band information, the proposed method produces clearer boundaries and more accurate category attributes in the classification results, with an overall accuracy of 96.20% and a Kappa coefficient of 0.955 6. This research can provide technical support for regional spatial planning and sustainable development.

Key words: land cover classification, random forest, feature optimizing, feature recursive elimination, Gini index

中图分类号:

P237

陈超, 梁锦涛, 杨刚, 孙伟伟, 龚绍军, 王建强. 面向土地覆盖精准分类的遥感特征参数优选方法[J]. 测绘学报, 2024, 53(7): 1401-1416.

Chao CHEN, Jintao LIANG, Gang YANG, Weiwei SUN, Shaojun GONG, Jianqiang WANG. Remote sensing parameters optimization for accurate land cover classification[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(7): 1401-1416.

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特征参数集	特征参数	卫星或数据源
光谱特征参数	Blue（蓝波段）、Green（绿波段）、Red（红波段）、VNIR（可见光-近红外波段）、SWIR1（短波红外波段1）、SWIR2（短波红外波段2）、NDVI（归一化差异植被指数）、EVI（增强植被指数）、RVI（比值植被指数）、SAVI（土壤调整植被指数）、NDBI（归一化差异建筑指数）、MNDWI（改进的归一化差异水体指数）	Sentinel-2
纹理特征参数	ASM（角二阶距）、CONT（对比度）、CORR（相关度）、VAR（方差）、IDM（逆差距）、SAVG（求和平均值）、SENT（总熵）、ENT（熵）、DENT（差分熵）	Sentinel-2
温热特征参数	LST（地表温度）、TIR1（热红外波段1）、TIR2（热红外波段2）	Landsat 8
高程特征参数	DEM（高程）、Slope（坡度）、Aspect（坡向）	SRTMV3
主成分特征参数	PC1（第一主成分分量）、PC2（第二主成分分量）、PC3（第三主成分分量）	Sentinel-2

土地利用类型	定义	样本的数量
建设用地	城乡居民点及工矿交通等用地	120
海洋	盐城东部的黄海区域	50
内陆水体	天然陆地水域、水利设施用地、养殖池塘	70
林地	树木、灌木和其他林地	80
耕地	可正常耕作的农田	150
裸地	地表为土壤或岩石，基本不被植被覆盖	120
滩涂	永久或季节性被浅水覆盖的区域	50
湿地	高潮位和低潮位之间的潮汐淹没区	100

土地利用类型	最高精度	最多特征	RF	CART	SVM	KNN
建设用地	1.000 0	1.000 0	0.985 9	1.000 0	1.000 0	0.956 5
海水	1.000 0	0.967 7	0.967 7	0.967 7	0.967 7	0.914 3
内陆水体	1.000 0	0.975 6	0.976 7	0.977 8	0.758 6	0.950 0
林地	0.958 3	0.938 8	0.913 0	0.938 8	0.938 8	0.938 8
耕地	0.955 6	0.966 3	0.943 8	0.945 1	0.955 6	0.913 0
裸地	0.927 6	0.869 6	0.840 6	0.759 5	0.864 9	0.783 8
滩涂	0.928 6	0.928 6	0.928 6	0.857 1	0.000 0	0.727 3
湿地	0.945 1	0.905 3	0.905 3	0.781 6	0.914 9	0.761 9

模型	总体精度/（%）	Kappa系数
本文模型	96.20	0.955 6
UNet	92.37	0.871 7
DeepLabv3+	93.50	0.889 8

面向土地覆盖精准分类的遥感特征参数优选方法

Remote sensing parameters optimization for accurate land cover classification

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