Abstract: To cultivate innovative talents in information disciplines under the background of emerging engineering education, an experimental project on detection and classification methods for pine wilt disease based on high- resolution satellite remote sensing imagery is designed, taking the “Digital Image Processing’’ and “Remote Sensing Principles and Applications’’ courses as the foundation, and combining with the “College Students’ Innovation and Entrepreneurship Training Program’’ of Jilin University. Using high-resolution satellite imagery from “Jilin-1’’, a pine wilt disease data sample library is established through image preprocessing. By comparing and analyzing the detection results of pine wilt disease using YOLOv5(You Only Look Once version 5), Faster R-CNN(Faster Region-Convolutional Neural Network), SSD(Single Shot MultiBox Detector), and YOLOv7 network models, the YOLOv7 network model is selected for optimization. The improved YOLOv7 network model has achieved a disease detection accuracy of 90. 66%. By analyzing the spectral characteristics of infected trees with different disease severity levels, SVM(Support Vector Machine)method is adopted to classify the severity of pine wilt disease, achieving a classification accuracy of 95. 44%. The results demonstrate that this experimentcan effectively help students integrate professional theoretical knowledge with practical techniques, enhance students’ practical innovation capabilities, and achieve the expected teaching outcomes.

Key words: digital image processing, remote sensing principles and applications, pine wilt disease, object detection, machine learning