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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (3) : 35-42     DOI: 10.6046/zrzyyg.2022493
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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
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Abstract  

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.
Keywords random forest      land use/land cover      index      principal component analysis      accuracy evaluation     
ZTFLH:  TP79  
Issue Date: 19 September 2023
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Articles by authors
Jintao LIANG
Chao CHEN
Zili ZHANG
Zhisong LIU
Cite this article:   
Jintao LIANG,Chao CHEN,Zili ZHANG, et al. A random forest-based method integrating indices and principal components for classifying remote sensing images[J]. Remote Sensing for Natural Resources, 2023, 35(3): 35-42.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022493     OR     https://www.gtzyyg.com/EN/Y2023/V35/I3/35
Fig.1  Location of study area and local comparison area
Fig.2  Flowchart of the study
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  Classification scheme, sample points and image features
Fig.3  Classification results of four methods
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  Classification accuracy of four machine learning algorithms
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  Area statistics of each category in four classification methods(km2)
Fig.4  Comparison of classification results in local area
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