Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

Author:

Li Jian1,Liu Hua12ORCID,Du Jia2,Cao Bin3,Zhang Yiwei2,Yu Weilin1,Zhang Weijian2,Zheng Zhi2ORCID,Wang Yan2,Sun Yue2,Chen Yuanhui4ORCID

Affiliation:

1. Computer Science and Technology, Faculty of Information Technology, Jilin Agricultural University, Changchun 130118, China

2. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

3. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China

4. College of Resource and Environment, Jilin Agricultural University, Changchun 130118, China

Abstract

The burning of straw is a very destructive process that threatens people’s livelihoods and property and causes irreparable environmental damage. It is therefore essential to detect and control the burning of straw. In this study, we analyzed Sentinel-2 data to select the best separation bands based on the response characteristics of clouds, smoke, water bodies, and background (vegetation and bare soil) to the different bands. The selected bands were added to the red, green, and blue bands (RGB) as training sample data. The band that featured the highest detection accuracy, RGB_Band6, was finally selected, having an accuracy of 82.90%. The existing object detection model cannot directly handle multi-band images. This study modified the input layer structure based on the YOLOv5s model to build an object detection network suitable for multi-band remote sensing images. The Squeeze-and-Excitation (SE) network attention mechanism was introduced based on the YOLOv5s model so that the delicate features of smoke were enhanced, and the Convolution + Batch normalization + Leaky ReLU (CBL) module was replaced with the Convolution + Batch normalization + Mish (CBM) module. The accuracy of the model was improved to 75.63%, which was 1.81% better than before. We also discussed the effect of spatial resolution on model detection and where accuracies of 84.18%, 73.13%, and 45.05% for images of 60-, 20-, and 10-m resolution, respectively, were realized. The experimental results demonstrated that the accuracy of the model only sometimes improved with increasing spatial resolution. This study provides a technical reference for the monitoring of straw burning, which is vital for both the control of straw burning and ways to improve ambient air quality.

Funder

National Key Research and Development Program of China

Science and Technology Project for Black Soil Granary

Environmental Protection Program of Jilin Province, China

Science and Technology Development Plan Project of Jilin Province

Science and Technology Development Plan of Changchun City

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference69 articles.

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