首页> 外文期刊>Fresenius environmental bulletin >FEATURE EXTRACTION METHOD OF ECOLOGICAL ENVIRONMENT LANDSCAPE REMOTE SENSING IMAGE BASED ON GIS TECHNOLOGY
【24h】

FEATURE EXTRACTION METHOD OF ECOLOGICAL ENVIRONMENT LANDSCAPE REMOTE SENSING IMAGE BASED ON GIS TECHNOLOGY

机译:基于GIS技术的生态环境景观遥感图像特征提取方法

获取原文
           

摘要

With the rapid development of social economy, science and technology, the rapid development of Chinese cities has led to the great changes of regional society and economy. Reducing the adverse effects of economic growth and social progress on ecologi-cal environment landscape pattern and promoting the virtuous cycle of natural resource system and social economic system have become the socialgoals. Eco-logical environment landscape image feature extrac-tion can provide timely and effective regional envi-ronment landscape information, which is important to analyze, predict and comprehensively evaluate the dynamic changes of landscape environment caused by the implementation of regional planning. In this paper, the Landsat-TM remote sensing images in 2010 and 2017 are used to extract and analyze the features of the remote sensing images by using the maximum likelihood classification method. The re-sults show that the overall classification accuracy and kappa coefficient of TM images in 2010 are 89.2409% and 0.8532.respectively, and the overall classification accuracy and kappa coefficient of TM images in 2017 are 86.2134%and 0.8012.respec-tively. The deviation rate of classified data in 2010 and2017 is not large. The deviation rate of woodland landscape is the smallest (2.89% for 2010 and 1.28%for 2017). followed by construction land landscape (3.54%for 2010 and 3.13%for 2017),and the devi-ation rate of other land landscape is the largest (7.85% for 2010 and 11.23% for 2017).
机译:随着社会经济,科学技术的快速发展,中国城市的快速发展导致了区域社会和经济的巨大变迁。降低经济增长和社会进步对生态钙环境景观模式的不利影响,促进自然资源系统的良性周期,社会经济制度已成为社会人。生态逻辑环境景观形象特征额外可以提供及时有效的区域环境狂热景观信息,这对于分析,预测和全面评估区域规划造成的景观环境的动态变化很重要。在本文中,2010年和2017年的Landsat-TM遥感图像用于通过使用最大似然分类方法提取和分析遥感图像的特征。重新陈述表明,2010年的TM图像的整体分类准确性和Kappa系数为89.2409%和0.8532. 2017年的TM图像的整体分类准确性和Kappa系数为86.2134%和0.8012.Respec-est。分类数据在2010年和2017年的偏差率并不大。林地景观的偏差率最小(2010年为2.89%,2017年的1.28%)。其次是建设用地景观(2017年3.54%和3.13%),其他土地景观的Devi-Ation率最大(2010年为7.85%,2017年的11.23%)。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号