Monitoring of marine debris in the Sea of Japan using multi-spectral satellite images

被引:2
|
作者
Aoyama, Takashi [1 ]
机构
[1] Fukui Univ Technol, Dept Elect Elect & Comp Engn, Fukui 9108505, Japan
关键词
marine debris; satellite remote sensing; scatter diagram; Sea of Japan;
D O I
10.1117/12.2068835
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The flow of marine debris in the ocean causes extensive damage to coastal environments. In addition to local rivers, a large proportion of the marine debris that washes up along the coastline of the Sea of Japan originates in neighboring countries. It is considered important to understand the flow of marine debris in the Sea of Japan for environmental research purposes and for international relations. This study describes the results of monitoring marine debris flows using multi-spectral satellite images. The small size of most marine debris means that it cannot be confirmed directly, even when using high spatial resolution satellite imagery. Thus, to extract candidate pixels containing possible marine debris, pixels with spectra that differ from those of the surrounding ocean and the wave crests were identified. As a first step towards monitoring marine debris, a method for identifying marine debris floating on the Sea of Japan has been proposed using two-dimensional scatter diagrams of satellite spectral bands.
引用
收藏
页数:7
相关论文
共 50 条
  • [21] Multi-variety maize maturity monitoring based on UAV multi-spectral images
    Jiang Y.
    Liu B.
    Zhang C.
    Zhao D.
    Chen R.
    Xu B.
    Long H.
    Yang G.
    Yang H.
    Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 2023, 39 (20): : 84 - 91
  • [22] Semantic Segmentation on Multi-Spectral Images
    Aslantas, Veysel
    Toprak, Ahmet Nusret
    Elmaci, Mehmet
    29TH IEEE CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS (SIU 2021), 2021,
  • [23] Large Scale Crop Classification from Multi-temporal and Multi-spectral Satellite Images
    Yilmaz, Ismail
    Imamoglu, Mumin
    Ozbulak, Gokhan
    Kahraman, Fatih
    Aptoula, Erchan
    2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2020,
  • [24] Multi-resolution and Multi-spectral analysis for Satellite Images Classification with Fuzzy Spatial relationships
    Mselmi, B.
    Rabah, Z. B.
    Farah, I. R.
    Solaiman, B.
    2014 FIRST INTERNATIONAL IMAGE PROCESSING, APPLICATIONS AND SYSTEMS CONFERENCE (IPAS), 2014,
  • [25] Fusion of multi-spectral and panchromatic images using fuzzy rule
    Yang, Xu-Hong
    Jing, Zhong-Liang
    Liu, Gang
    Hua, Li-Zhen
    Ma, Da-Wei
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2007, 12 (07) : 1334 - 1350
  • [26] Classification of Materials in Natural Scenes using Multi-Spectral Images
    Namin, Sarah Taghavi
    Petersson, Lars
    2012 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2012, : 1393 - 1398
  • [27] Segmentation of multi-spectral images using the combined classifier approach
    Paclík, P
    Duin, RPW
    van Kempen, GMP
    Kohlus, R
    IMAGE AND VISION COMPUTING, 2003, 21 (06) : 473 - 482
  • [28] Research on Detection of Ship Target at Sea Based on Multi-Spectral Infrared Images
    Qiu Rong-chao
    Lou Shu-li
    Li Ting-jun
    Gong Jian
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39 (03) : 698 - 704
  • [29] Segmentation of spectral objects from multi-spectral images using canonical analysis
    Lira, J
    Rodriguez, A
    2003 IEEE WORKSHOP ON ADVANCES IN TECHNIQUES FOR ANALYSIS OF REMOTELY SENSED DATA, 2004, : 86 - 91
  • [30] A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images
    Abady, Lydia
    Dimitri, Giovanna Maria
    Barni, Mauro
    REMOTE SENSING, 2024, 16 (05)