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

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 17; p. 3330
• Main Authors: Wang, Rui, Ren, Jiahao, Li, Weidong, Yu, Teng, Zhang, Fan, Wang, Jiangtao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Altitude; Automation; Behavior; concentration identification; Convolution; Data analysis; Deformation effects; entomological radar; Entomology; Feature extraction; Formability; Insect migration; Insects; Instance segmentation; Methods; Modules; Neural networks; Phased arrays; Radar; Radar arrays; Radar data; Radar systems; Receptive field; Semantics; Spatial distribution; Spatiotemporal data; Technology assessment; Temporal distribution; United Kingdom > UK; Australia
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16173330

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.