FUNCTION ASSIGNMENT OF PLASTICS BASED ON HYPERSPECTRAL SATELLITE IMAGES AND HIGH-RESOLUTION DATA USING DEEP LEARNING ALGORITHMS

被引:0
|
作者
Zhou, Shanyu [1 ,2 ]
Mou, Lichao [1 ]
Zhang, Lixian [3 ]
Hua, Yuansheng [1 ]
Kaufmann, Hermann [2 ]
Zhu, Xiaoxiang [1 ]
机构
[1] Tech Univ Munich, Data Sci Earth Observat, D-80333 Munich, Germany
[2] German Res Ctr Geosci GFZ, Remote Sensing & Geoinformat, D-14473 Potsdam, Germany
[3] Tsinghua Univ, Dept Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China
来源
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年
关键词
Sentinel-2; Plastic detection; Deep learning; Image processing; Classification;
D O I
10.1109/IGARSS52108.2023.10283116
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Plastic pollution is becoming an increasingly prominent problem and the function of plastics determines whether they need to be recycled or not. In order to explore the possibility of using satellite imagery to classify the functionality of plastics, this study proposes a two-stage workflow: firstly, a classification map is obtained based on hyperspectral satellite imagery to generate plastic types, and then using these identified plastic coverage areas, a deep learning algorithm is used to assign functionality to these classified plastic areas based on sentinel-2 imagery. By comparing five leading-edge image classification models, classification accuracies of up to 74% were achieved, demonstrating the feasibility of using deep learning models trained on satellite images to identify plastic features.
引用
收藏
页码:7257 / 7260
页数:4
相关论文
共 50 条
  • [1] Mapping taluses using deep learning and high-resolution satellite images
    Jiang, Decai
    Feng, Min
    Yan, Dezhao
    Wang, Yingzheng
    Xu, Jinhao
    Wang, Ning
    Wang, Jianbang
    Li, Xin
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2025, 18 (01)
  • [2] Semantic segmentation of high-resolution satellite images using deep learning
    Kuldeep Chaurasia
    Rijul Nandy
    Omkar Pawar
    Ravi Ranjan Singh
    Meghana Ahire
    Earth Science Informatics, 2021, 14 : 2161 - 2170
  • [3] Semantic segmentation of high-resolution satellite images using deep learning
    Chaurasia, Kuldeep
    Nandy, Rijul
    Pawar, Omkar
    Singh, Ravi Ranjan
    Ahire, Meghana
    EARTH SCIENCE INFORMATICS, 2021, 14 (04) : 2161 - 2170
  • [4] GEOMEMBRANE BASINS DETECTION BASED ON SATELLITE HIGH-RESOLUTION IMAGERY USING DEEP LEARNING ALGORITHMS
    Benayad, Mohamed
    Houran, Nouriddine
    Aamir, Zakaria
    Maanan, Mehdi
    Rhinane, Hassan
    GEOINFORMATION WEEK 2022, VOL. 48-4, 2023, : 75 - 79
  • [5] Identifying thermokarst lakes using deep learning and high-resolution satellite images
    Zhang, Kuo
    Feng, Min
    Sui, Yijie
    Xu, Jinhao
    Yan, Dezhao
    Hu, Zhimin
    Han, Fei
    Sthapit, Earina
    SCIENCE OF REMOTE SENSING, 2024, 10
  • [6] Identifying reservoirs in northwestern Iran using high-resolution satellite images and deep learning
    Shi, Kaidan
    Su, Yanan
    Xu, Jinhao
    Sui, Yijie
    He, Zhuoyu
    Hu, Zhongyi
    Li, Xin
    Vereecken, Harry
    Feng, Min
    GEO-SPATIAL INFORMATION SCIENCE, 2024, 27 (03): : 922 - 933
  • [7] Marine Bird Detection Based on Deep Learning using High-Resolution Aerial Images
    Ben Boudaoud, Lynda
    Maussang, Frederic
    Garello, Rene
    Chevallier, Alexis
    OCEANS 2019 - MARSEILLE, 2019,
  • [8] Building footprint extraction from very high-resolution satellite images using deep learning
    Ps, Prakash
    Aithal, Bharath H.
    JOURNAL OF SPATIAL SCIENCE, 2023, 68 (03) : 487 - 503
  • [9] A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images
    Mo, Shaoyi
    Shi, Yufeng
    Yuan, Qi
    Li, Mingyue
    SENSORS, 2024, 24 (05)
  • [10] DEEP LEARNING-BASED STEREO MATCHING FOR HIGH-RESOLUTION SATELLITE IMAGES: A COMPARATIVE EVALUATION
    He, X.
    Jiang, S.
    He, S.
    Li, Q.
    Jiang, W.
    Wang, L.
    GEOSPATIAL WEEK 2023, VOL. 48-1, 2023, : 1635 - 1642