Remote sensing monitoring method of livestock in grassland based on multi-scale features and multi-models fusion

XIAO Rulin; GAO Jixi; LIU Aijun; HOU Peng; ZHANG Wenguo; YANG Yong; LI Yunbao; FU Zhuo; JIN Chuanping; YANG Xu; ZHENG Shuhua; YIN Shoujing

doi:10.11834/jrs.20222099

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Remote sensing monitoring method of livestock in grassland based on multi-scale features and multi-models fusion
Vol. 27, Issue 10, Pages: 2383-2394(2023)
Received：11 March 2022，

Published：07 October 2023
DOI： 10.11834/jrs.20222099
稿件说明：

移动端阅览

肖如林，高吉喜，刘爱军，侯鹏，张文国，杨勇，李运保，付卓，靳川平，杨栩，郑淑华，殷守敬.2023.多尺度特征和多模型相融合的草原区牧畜遥感监测.遥感学报，27（10）： 2383-2394 DOI： 10.11834/jrs.20222099.

Xiao R L， Gao J X， Liu A J， Hou P， Zhang W G， Yang Y， Li Y B， Fu Z， Jin C P， Yang X， Zheng S H and Yin S J. 2023. Remote sensing monitoring method of livestock in grassland based on multi-scale features and multi-models fusion. National Remote Sensing Bulletin， 27（10）：2383-2394 DOI： 10.11834/jrs.20222099.

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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