Application of Instance Segmentation to Identifying Insect Concentrations in Data from an Entomological Radar

Author:

Wang Rui12,Ren Jiahao1,Li Weidong12,Yu Teng12,Zhang Fan1,Wang Jiangtao1

Affiliation:

1. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

2. Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China

Abstract

Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation of layer concentrations. The advent of High-Resolution Phased Array Radar (HPAR) has transformed this situation. With its high range resolution and high data update rate, HPAR can generate detailed concentration spatiotemporal distribution heatmaps. This technology facilitates the detection of changes in insect concentrations across different time periods and altitudes, thereby enabling the observation of large-scale take-off, landing, and layering phenomena. However, the lack of effective techniques for extracting insect concentration data of different phenomena from these heatmaps significantly limits detailed analyses of insect migration patterns. This paper is the first to apply instance segmentation technology to the extraction of insect data, proposing a method for segmenting and extracting insect concentration data from spatiotemporal distribution heatmaps at different phenomena. To address the characteristics of concentrations in spatiotemporal distributions, we developed the Heatmap Feature Fusion Network (HFF-Net). In HFF-Net, we incorporate the Global Context (GC) module to enhance feature extraction of concentration distributions, utilize the Atrous Spatial Pyramid Pooling with Depthwise Separable Convolution (SASPP) module to extend the receptive field for understanding various spatiotemporal distributions of concentrations, and refine segmentation masks with the Deformable Convolution Mask Fusion (DCMF) module to enhance segmentation detail. Experimental results show that our proposed network can effectively segment concentrations of different phenomena from heatmaps, providing technical support for detailed and systematic studies of insect migration behavior.

Funder

National Key R&D Program of China

Publisher

MDPI AG

Reference51 articles.

1. Radar Aeroecology: Exploring the Movements of Aerial Fauna through Radio-Wave Remote Sensing;Chilson;Biol. Lett.,2012

2. Mass Seasonal Bioflows of High-Flying Insect Migrants;Hu;Science,2016

3. Drake, V.A., and Reynolds, D.R. (2012). Radar Entomology: Observing Insect Flight and Migration, Cabi.

4. Recent Insights from Radar Studies of Insect Flight;Chapman;Annu. Rev. Entomol.,2011

5. Radar Studies of Locust, Moth and Butterfly Migration in the Sahara;Schaefer;Proc. R. Entomol. Soc. Lond. Ser. C,1969

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3