干旱区生态环境知识资源中心(Arid): 基于纹理特征与LSSVM的青土湖地物提取

Arid

	基于纹理特征与LSSVM的青土湖地物提取
其他题名	Feature extraction of Qingtu Lake based on texture feature and Least Squares Support Vector Machine
	张华; 王敏
来源期刊	干旱区地理
ISSN	1000-6060
出版年	2018
卷号	41 期号:4 页码:802-808
中文摘要	纹理特征作为一种非光谱信息能够增强地物之间的特征差异,这对于高分辨率遥感影像的地物提取有着重要意义。以青土湖为研究区,以Worldview- 2影像为数据源,通过引入权重因子定义联合概率函数来确定最佳窗口尺度,利用灰度共生矩阵提取最佳窗口尺度下的纹理特征,将其与原始遥感影像合成,采用最小二乘支持向量机(LSSVM)进行地物提取,将提取结果与仅利用光谱信息的支持向量机(SVM)提取结果、辅以纹理特征的SVM提取结果对比分析。结果表明:此方法可以更加快速准确地提取青土湖地物,精度高达85.86%,优于仅利用光谱信息的SVM的65.13%,辅以纹理特征的SVM的73.45%,可为地物破碎的干旱区高分辨率遥感影像地物提取提供有益借鉴。
英文摘要	In this paper,the study area Qingtu Lake is located at Minqin County,Gansu Province,China. Based on Worldview - 2 remote sensing images and the field measured data,using texture features and LSSVM method,the object information of Qingtu Lake in arid area were extracted. The texture features as a non spectral information can enhance the difference between the object features,which has important meaning for object extraction from high resolution remote sensing image,but the key to texture feature extraction is to choose the optimal window scale,which directly influences the accuracy of subsequent extraction. Therefore this paper introduced a weighting factor to define joint probability function to determine the optimal window scale,adopted gray level co-occurrence matrix to extract texture features at the optimal window scale,and then synthesized with the original remote sensing image. Because LSSVM is a good solution to the robustness,sparsity and large-scale computing problems of SVM,the paper adopted the LSSVM method to extract the object information of the Qingtu Lake,and compared the results with that of the feature extraction only using the spectral information of SVM and the extraction of SVM combined with texture feature. The results of comparison show that this method of LSSVM can more quickly and accurately extract the object information of the Qingtu Lake,the total precision was 85.86%,Kappa was 0.822 5,better than SVM only using the spectral information with the total precision 65.13%,and SVM combined with texture features with 73.45%. In general,the LSSVM could be more useful to extract information of the fragmented objects in arid area from high resolution remote sensing image.
中文关键词	Worldview- 2影像 ; 纹理特征 ; 权重 ; 最小二乘支持向量机 ; 青土湖
英文关键词	Worldview - 2 image texture feature weight LSSVM Qingtu Lake
语种	中文
国家	中国
收录类别	CSCD
WOS类目	REMOTE SENSING
WOS研究方向	Remote Sensing
CSCD记录号	CSCD:6312508
来源机构	西北师范大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/237604
作者单位	西北师范大学地理与环境科学学院, 兰州, 甘肃 730070, 中国
推荐引用方式 GB/T 7714	张华,王敏. 基于纹理特征与LSSVM的青土湖地物提取[J]. 西北师范大学,2018,41(4):802-808.
APA	张华,&王敏.(2018).基于纹理特征与LSSVM的青土湖地物提取.干旱区地理,41(4),802-808.
MLA	张华,et al."基于纹理特征与LSSVM的青土湖地物提取".干旱区地理 41.4(2018):802-808.