A random forest-based method integrating indices and principal components for classifying remote sensing images
LIANG Jintao1(), CHEN Chao2(), ZHANG Zili3, LIU Zhisong4
1. Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China 2. School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China 3. Zhejiang Ecological and Environmental Monitoring Center (Zhejiang Key Laboratory of Ecological and Environmental Monitoring, Forewarning and Quality Control), Hangzhou 310012, China 4. School of Information Engineering, Zhejiang Ocean University, Zhoushan 316022, China
Accurate information about land use/land cover (LULC) can provide significant guidance for regional spatial planning and sustainable development. However, conventional methods for remote sensing image classification are challenging due to complex surface morphologies, diverse surface feature types, and nonlinear features of remote sensing images. Therefore, they fail to fully utilize the rich information in remote sensing images. This study developed a random forest-based classification method for remote sensing images to extract LULC information by integrating indices and principal components. First, the images covering the study area were selected to determine cloud cover and conduct median synthesis of images, obtaining interannual remote sensing images. Then, various calculated indices and the extracted principal components were integrated into the band stacks of remote sensing images. Furthermore, classifiers were constructed using different machine-learning algorithms. Finally, based on a confusion matrix, the classification results were evaluated using overall accuracy and the Kappa coefficient. The experimental results of the Hangzhouwan area show that the decision support based on vegetation, water, building indices, and principal components can improve the classification accuracy, yielding overall accuracy and Kappa coefficient of 91.42% and 0.894 2, respectively, which were higher than those of conventional methods such as random forest, classification and regression tree, and support vector machine. The method for remote sensing image classification proposed in this study, which integrates indices and principal components, can obtain high-accuracy land use classification results by accurately extracting land cover features in remote sensing images. This study will provide method support for fine-scale surface classification.
梁锦涛, 陈超, 张自力, 刘志松. 一种融合指数与主成分分量的随机森林遥感图像分类方法[J]. 自然资源遥感, 2023, 35(3): 35-42.
LIANG Jintao, CHEN Chao, ZHANG Zili, LIU Zhisong. A random forest-based method integrating indices and principal components for classifying remote sensing images. Remote Sensing for Natural Resources, 2023, 35(3): 35-42.
Fig.1 研究区以及局部对比区域 (Landsat8 OLI B7(R),B6(G),B4(B)彩色合成)
Fig.2 研究流程
类别
定义
样本 点/个
遥感图像特征 (标准假彩色合成)
建设用地
城乡居民点及工矿交通等用地
210
林地
树木、灌木和其他林地
220
水体
自然陆地水域、水利设施用地和养殖池塘
130
耕地
长年可正常耕作的农田
250
裸地
地表为土壤或岩石,土地基本不被植被覆盖
100
滩涂
海高低潮位之间的潮汐淹没区
100
Tab.1 分类方案、样本点及图像特征
Fig.3 4种方法分类结果
类别
本文方法
RF
CART
SVM
OA/%
91.42
88.78
83.17
77.23
Kappa系数
0.894 2
0.861 5
0.792 6
0.716 1
Tab.2 4种机器学习算法分类精度对比
类别
建设用地
林地
水体
耕地
裸地
滩涂
ESA
2 851.35
2 884.21
605.99
3 812.34
1 274.23
141.40
本文方法
2 984.76
1 881.52
680.21
4 800.59
1 078.13
161.50
RF
2 545.11
1 776.72
675.00
5 398.44
1 029.98
166.13
CART
2 964.11
1 803.62
1 216.39
4 594.25
848.88
174.46
SVM
3 507.76
1 939.85
438.10
5 539.48
1.68
163.52
Tab.3 4种分类方法各类别面积统计
Fig.4 局部区域分类结果对比
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2023, Vol. 35 
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