Most remote-sensing systems obtain terrain surface images from vertical observation by satellites and high-altitude aircrafts. However, this can be inadequate for subsurface geological image acquisition. Indeed, subsurface or undersurface geological information is more essential for geological and civil engineering. In this work, we proposed a subsurface geological feature recognition approach: first, cross-section geological images are captured horizontally by low-altitude UAV. Later, deep transfer learning model is trained to classify the type of geological mass exposed on the cross-section. We also examined different deep transfer learning models regarding their classification performances. The result suggests that Inception-ResNet-V2 based model outperformed others by reaching an overall accuracy of 99.72%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Low-Altitude UAV Imagery Based Cross-Section Geological Feature Recognition via Deep Transfer Learning


    Contributors:
    Zhao, Yi (author) / Li, Jiandong (author) / Li, Xiaohui (author) / Hu, Yingdi (author)


    Publication date :

    2018-11-01


    Size :

    323154 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Deep transfer learning for underwater vehicle wake recognition in infrared imagery

    Rui, Zhong / Li, Yang / Du, Yongcheng | British Library Conference Proceedings | 2019



    Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning

    Shanwei Niu / Zhigang Nie / Guang Li et al. | DOAJ | 2024

    Free access

    Low-altitude Fixed-wing UAV Obstacle Recognition Based on Deep Learning

    Ding, Dian / Wang, Yangzhu / Xiao, Yao et al. | IEEE | 2018