多尺度特征和多模型相融合的草原区牧畜遥感监测
Remote sensing monitoring method of livestock in grassland based on multi-scale features and multi-models fusion
- 2023年27卷第10期 页码：2383-2394
收稿：2022-03-11，
纸质出版：2023-10-07
DOI： 10.11834/jrs.20222099
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摘要
超载过牧是中国草原退化的主要原因之一，而载畜情况是评估草畜平衡的关键。面向牧畜高效、精准监管工作需求，针对牧畜“小（微）目标”监测的痛点难点，基于亚米级卫星遥感数据，综合利用牧畜“点”特征、“群”特征、“移动不固定”特征等多种特征，融合深度学习、面向对象等多种识别技术方法，构建了一种多尺度特征和多模型方法相融合的牧畜高分卫星遥感监测技术方法。该方法通过对牧畜弱信号的有效增强、“牧畜群”和“牧畜斑点”的分阶段检测与相互融合增强，实现了对牧畜群分布、牧畜斑点分布和牧畜群规模的监测提取，推动牧畜卫星遥感监测向“点数”式精细化监测迈进。锡林郭勒草原区域监测实验数据显示，模型检出率约0.802，误检率约0.244，具有较好效果。该方法的应用，可为草原区载畜情况的监测监管提供支撑，也可为其他“小（微）目标”的遥感监测提供借鉴；无论是在技术创新还是业务应用方面都具有十分重要的意义。
Abstract
China is a large country of grassland and animal husbandry. Overloading and overgrazing is one of the main causes of grassland degradation in China. To protect the grassland
it is necessary to precisely monitoring livestock carrying capacity
which is the key to evaluation and control grass-livestock balance. However
traditional method of livestock carrying capacity such as hierarchical statistics
sampling field survey and online camera monitoring is whether time-consuming
labor-intensive
costly or poor quality. So it is very urgent to find a kind of efficient and precise monitoring method of livestock carrying capacity in grassland.
To achieve this goal
this research proposes an efficient and precise monitoring method of the livestock in grassland by using of sub-meter resolution satellite image. The method not only fuses multi-scale features of livestock in sub-meter resolution satellite image such as “blob feature”、“flock feature”、“moving feature”
but also integrates deep learning technology and object oriented recognition technology. Firstly
considering that the livestock in satellite image is a kind of small (tiny) target
it uses kinds of image enhancement method such as bi-lateral filtering and Laplace of Gaussian (LoG) operator to enhance the weak livestock signal successfully. Secondly
in consideration of “flock feature” of livestock flock
it use a kind of “livestock flock detection model” based on deep learning technology to get the rough distribution area of livestock flocks. Thirdly
in consideration of “blob feature” and “moving feature” of livestock
it use a kind of “livestock blob detection method” based on LoG Gradient Difference and object oriented recognition technology to get the possible livestock blobs. Finally
by integrating the detecting result of both livestock flocks and livestock blobs
it uses the livestock blobs result to enhance and verify the livestock flocks result
and the enhanced and verified livestock flocks and the livestock blobs within them is finally get by using some simple manually revising work.
Through an experiment in Xilingol grassland
it is found that the approach has good effect on livestock flock detection: with positive detection rate about 0.802 and false detection rate about 0.244
especially as to big livestock flock
the positive detection rate is up to 0.937
the false detection rate is low to 0.072.
It is very helpful for the monitoring and supervision of livestock flock in grassland
and can also provide reference for remote sensing monitoring of other “small (tiny) targets”. It is of great significance both in terms of technological innovation and business application. It makes a litter effort in promoting the livestock satellite remote sensing monitoring into an intuitive and fine monitoring era-“Number-Counting Era”.
关键词
Keywords
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肖如林 生态环境部卫星环境应用中心
高吉喜 生态环境部卫星环境应用中心
刘爱军 内蒙古自治区草原遥感及应急技术储备重点实验室
侯鹏 生态环境部卫星环境应用中心
张文国 生态环境部卫星环境应用中心
杨勇 内蒙古自治区草原遥感及应急技术储备重点实验室
李运保 绵阳市生态环境局信息中心
付卓 生态环境部卫星环境应用中心
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西南交通大学 地球科学与工程学院
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