基于特征空间优化的随机森林算法在GF-2影像湿地分类中的研究

doi:10.12082/dqxxkx.2018.180119

摘要/Abstract

摘要：

由于季节性的植被动态和水文波动,湿地遥感影像分类常常比较困难。本文采用优化特征空间的随机森林算法（Random Forest）对吉林省白城市通榆县东部地区预处理后的GF-2影像进行湿地分类研究,具体分为2步：① 对研究区遥感影像进行多尺度分割和对象特征的提取。针对一些学者获取最佳分割尺度时仍受主观因素影响较大的情况,本文通过改进全局最优分割方法来获得最佳分割尺度。② 在最优分割的基础上,基于特征重要性对随机森林分类算法的特征空间进行优化,以得到最佳的随机森林分类结果,并与相同条件下（同数据、同分割尺度、同训练样本,同特征空间）的K-NN、SVM、CART 3种算法以及未优化特征空间的RF算法的分类结果进行了比较。结果表明,基于优化特征空间的RF算法的分类结果总精度和Kappa系数分别为93.038%和0.9177,而K-NN、SVM和CART 3种分类算法的分类结果的总精度分别为83.357%、78.068%、77.136%,未优化特征空间的RF算法分类结果总精度为90.937%。相较于K-NN、SVM、CART 3种分类算法,RF算法在GF-2湿地影像数据中具有更好的分类性能,同时优化特征空间的RF算法精度有所提高,在湿地资源管理中可以发挥非常重要的作用。

关键词: GF-2影像, 面向对象, 随机森林, 湿地分类, 最优分割尺度, 特征空间优化

Abstract:

Due to seasonal vegetation dynamics and hydrological fluctuations, classification of wetland from remote sensing images is often more difficult. In this paper, a pretreated GF-2 image in the east of Tongyu Country, Baicheng City, Jilin Province, was analyzed by Random Forest with optimized feature space. The key method is divided into two steps. The first step is to perform multi-scale segmentation and extraction of object features in the remote sensing image of the study area. For a situation that some scholars obtain the best segmentation scale subjected to subjective factors, this paper obtains the best segmentation scale by improving the global optimal segmentation method, The second step is based on optimal segmentation, to optimize the feature space of the random forest classification algorithm on the basis of the importance of features to obtain the best random forest classification results, and then the classification results of the K-NN, SVM, and CART algorithms with the same data, the same segmentation scale, the same training sample and the same feature space, and the RF algorithm with unoptimized feature space are compared. The results show that the total classification accuracy and Kappa coefficient of the RF algorithm based on optimized feature space are 93.038% and 0.9177, respectively, while the total accuracy of the classification results of K-NN, SVM and CART are 83.357% and 78.068%, respectively, 77.136%, the total accuracy of the classification results of RF algorithm with unoptimized feature space is 90.937%. Compared with K-NN, SVM and CART classification algorithms, the RF algorithm has better classification performance in GF-2 wetland image data. At the same time, the accuracy of the RF algorithm with the optimized feature space has been improved, and it can play a very important role in wetland resource management.

Key words: GF-2 Imagery, object-oriented, random forest, wetland classification, optimal segmentation scale, feature space optimization

詹国旗, 杨国东, 王凤艳, 辛秀文, 国策, 赵强. 基于特征空间优化的随机森林算法在GF-2影像湿地分类中的研究[J]. 地球信息科学学报, 2018, 20(10): 1520-1528.DOI:10.12082/dqxxkx.2018.180119

ZHAN Guoqi,YANG Guodong,WANG Fengyan,XIN Xiuwen,GUO Ce,ZHAO Qiang. The Random Forest Classification of Wetland from GF-2 Imagery Based on the Optimized Feature Space[J]. Journal of Geo-information Science, 2018, 20(10): 1520-1528.DOI:10.12082/dqxxkx.2018.180119

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类型名称		类型含义
自然湿地	盐碱泡	盐碱化区域、含盐分较高的湖泊坑塘
	杂草盐生沼泽	位于盐碱泡周边、盐碱地上,耐盐植被覆盖
	香蒲沼泽	距离水泡附近,以香蒲植被为主的植被沼泽,呈现暗绿色
	草丛沼泽	分布在香蒲沼泽与耕地之间,覆盖草丛植被
非湿地	盐碱裸地	表层盐碱聚集,基本没有植被的土地
	旱地	无灌溉设施,依靠天然水源的耕地
	树林	包含旱地周边的人工树林和天然树林
	房屋	包括达到2×2个像元的建筑
	道路	主要为农村之间修筑的水泥道路

主成分PC	特征值	累积特征值百分比/%	特征值百分比/%
1	40 101.956 3	91.27	91.27
2	3 572.032 7	99.40	8.13
3	222.299 9	99.91	0.51
4	40.822 0	100.00	0.09

特征名称
光谱特征	Mean B、Mean G、Mean R、Mean NIR、Standard deviation B、Standard deviation G、Standard deviation R、Standard deviation NIR、Brightness、AVE(B、R、G 3波段均值) 、Ratio to scene R、Ratio to scene G、Ratio to scene B、Ratio to scene NIR
几何特征	Area、Length/Width、Width、Asymmetry、Border index、Compactness、Density、Rectangular Fit、Shape index、Number of edges(polygon) 、Stddev of length of edges(polygon)
纹理特征	GLCM Homogeneity PC2(all dir.) 、GLCM Contrast PC2(all dir.) 、GLCM Dissimilarity PC2(all dir.) 、GLCM Entropy PC2(all dir.) 、GLCM Ang.2nd moment PC2(all dir.) 、GLCM Mean PC2(all dir.) 、GLCM StdDev PC2(all dir.) 、GLDV Entropy PC2(all dir.) 、GLDV Ang.2nd moment PC2(all dir.) 、GLDV Mean PC2(all dir.) 、GLDV Contrast PC2(all dir.)
自定义特征	BRITHTEN DIFFER(相邻对象亮度差)、SR、SRWC、percent(B)、percent(G)、percent(R)、Max.diff、Mean NDVI、Mean NDWI、Mean PC1、Mean PC2、Standard deviation NDVI、Standard deviation NDWI、Standard deviation PC1、Standard deviation PC2

类别	错分误差	漏分误差	制图精度	用户精度
盐碱泡	0	0	100	100
香蒲沼泽	6.02	26.6	73.4	93.98
杂草盐生沼泽	13.2	12.79	87.21	86.8
草丛沼泽	24.19	3.3	96.7	75.81
盐碱裸地	0	2.01	97.99	100
林地	0	0	100	100
旱地	2.63	1.19	98.81	97.37
建筑物	0	0	100	100
道路	8.11	0	100	91.87