Classification method of land cover and irrigated farm land use based on UAV remote sensing in irrigation

被引：0

作者：

Han W. ^{[1
,2
]}

Guo C. ^{[1
]}

Zhang L. ^{[1
]}

Yang J. ^{[3
]}

Lei Y. ^{[1
]}

Wang Z. ^{[1
]}

机构：

[1] College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, Shaanxi

[2] Institute of Soil and Water Conservation, Northwest A&F University, Yangling, 712100, Shaanxi

[3] College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, 712100, Shaanxi

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2016年 / 47卷 / 11期

关键词：

Irrigated farm land use; Land cover classification; SVM; UAV remote sensing; Visible band;

D O I：

10.6041/j.issn.1000-1298.2016.11.037

中图分类号：

学科分类号：

摘要：

In order to verify the availability of UAV(unmanned aerial vehicle) optical remote sensing technology in land use type and classification, Wuyuan county Tal Lake town of Hetao Irrigation Area was chosen as research area and visible images were obtained by using TEZ fixed wing UAV equipment with SONY A5100. After obtaining the visible high resolution images by using the UAV remote sensing system, they were mosaicked in the Agisoft PhotoScan software. In addition to visually extracting ground object, we also adopted object oriented which segmentation scale was 300, shape factor was 0.4, smoothness was 0.5 to divide images. On the basis of visual, according to the specificity of ground object in spectrum, shape and texture feature, we respectively established decision tree, support vector machine, K-nearest neighbor classification to extract land use type. Results indicated that SVM can accurately extract characteristics of ground object, the overall accuracy was 82.20%, Kappa coefficient was 0.765 9; overall accuracy and Kappa coefficient of decision tree were 74.00% and 0.667 5, respectively; overall accuracy and Kappa coefficient of K-nearest neighbor classification were 71.40% and 0.610 7, respectively. In this paper, based on the support vector machine classification method combined with the decision tree model, the overall accuracy was grown up to 84.20%, Kappa coefficient reached 0.790 0. But there existed the wrong situation of small trench being divided into traffic and transport. The visible UAV remote sensing technology can be used to extract the irrigated land use types, but the extraction ditches need further study. © 2016, Chinese Society of Agricultural Machinery. All right reserved.

引用

页码：270 / 277

页数：7

共 26 条

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