Mapping regional cropping patterns by using GF-1 WFV sensor data

SONG Qian; ZHOU Qing-bo; WU Wen-bin; HU Qiong; LU Miao; LIU Shu-bin

首页> 外文期刊>农业科学学报（英文版） >Mapping regional cropping patterns by using GF-1 WFV sensor data

【24h】

Mapping regional cropping patterns by using GF-1 WFV sensor data

机译：使用GF-1 WFV传感器数据绘制区域种植模式

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

The successful launched Gaofen satelite no. 1 wide ifeld-of-view (GF-1 WFV) camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei’an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identiifcation and explore how to fuly use its spectral, textural and temporal information to improve classiifcation accuracy. In doing so, an object-based and Random Forest (RF) algorithm was used for crop mapping. The results showed that classiifcation based on an optimized single temporal GF-1 image can achieve an overal accuracy of about 83%, and the addition of textural features can im-prove the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classiifcation map of crops with an overal accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classiifcation results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efifciency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.

机译：成功推出的高芬卫星没有。 1个宽的IFELD视图（GF-1 WFV）摄像机的特点是其高空间分辨率，并且可以为区域作物映射提供一些潜力。这项研究以北安市为东北地区作为研究领域，旨在调查GF-1 WFV图像的潜力，用于作物识别，探索如何富发使用其光谱，纹理和时间信息来提高分类精度。这样做，基于对象和随机林（RF）算法用于裁剪映射。结果表明，基于优化的单个时间GF-1图像的分类可以达到约83％的高精度，并且纹理特征的增加可以通过8.14％来证明精度。此外，多时间GF-1数据可以产生93.08％的高精度的作物的分类图，并将纹理变量引入多时间GF-1数据只能将准确性提高约1％，这表明与单个时间数据相比，GF-1用于裁剪映射的时间信息的重要性。通过使用不同的特征输入进行比较GF-1数据的分类结果，得出结论，GF-1 WFV数据一般可以满足区域作物的映射互感和准确性要求。但是，鉴于GF-1 WFV图像的独特光谱特性，需要使用文本和时间信息来产生令人满意的精度。

著录项

来源
《农业科学学报（英文版）》 |2017年第2期|337-347|共11页
作者
SONG Qian; ZHOU Qing-bo; WU Wen-bin; HU Qiong; LU Miao; LIU Shu-bin;
展开▼
作者单位

Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China;

Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China;

Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China;

Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China;

Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China;

Remote Sensing Technology Center, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, P.R.China;

展开▼
收录信息中国科学引文数据库(CSCD);中国科技论文与引文数据库(CSTPCD);
原文格式 PDF
正文语种 eng
中图分类
关键词

Mapping regional cropping patterns by using GF-1 WFV sensor data

摘要

著录项

相关主题

期刊订阅