Exploring Various Applicable Techniques to Detect Smoke on the Satellite Images

被引:3
|
作者
Huang, Chau-Lin [1 ]
Munasinghe, Thilanka [1 ]
机构
[1] Rensselaer Polytech Inst, Dept Informat & Technol & Web Sci, Troy, NY 12180 USA
来源
2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2020年
关键词
smoke detection; wildfire; scene classification; object localization; CNN; CONVOLUTIONAL NETWORKS;
D O I
10.1109/BigData50022.2020.9378466
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Every year, the wildfires ravage broad areas of natural forest and nearby regions, causing substantial financial and life losses and deteriorating the air quality. More air hazards are emitted to the atmosphere reaching as high as the stratosphere propagating through the air currents. With the aggravation of climate change, wildfires of either human or natural cause could become more ferocious and devastating. A feasible solution is to detect the wildfire and respond early before the fire spread becomes irreversible. Satellite imagery serves as a cost-effective means to update near-real-time holistic landscape views of land and sea over extended periods. Such an advantage makes early fire detection and warning even in remote areas possible. The rendered images provided by the satellites' various instruments incorporate various channels to provide real and artificial colors to reveal landscape details imperceptible to the naked eyes. This imagery dataset discussed in this paper derives from NASA's Aqua and Terra satellites and make available at the NASA-IMPACT data share repository [1]. The dataset totals 704 images of cropped frames and their labeled images taken during both satellites' extensive flyby observations. The images also contain spatial-temporal information serving as relevant metadata for analysis. This paper provides a survey of the recent advances in neural network-based object detection techniques followed by machine learning and deep learning-based methods to detect and localize smoke. A comprehensive elaboration of the datasets follows the method overview.
引用
收藏
页码:5703 / 5705
页数:3
相关论文
共 50 条
  • [31] Data mining techniques on satellite images for discovery of risk areas
    Traore, Boukaye Boubacar
    Kamsu-Foguem, Bernard
    Tangara, Fana
    EXPERT SYSTEMS WITH APPLICATIONS, 2017, 72 : 443 - 456
  • [32] Evaluating dehazing techniques on artificial and satellite land surface images
    Fridvalszky, Andras
    Toth, Balazs
    Szecsi, Laszlo
    IDOJARAS, 2023, 127 (04): : 447 - 457
  • [33] Using Machine Learning Techniques to Detect Defects in Images of Metal Structures
    V. E. Dementev
    M. N. Suetin
    M. A. Gaponova
    Pattern Recognition and Image Analysis, 2021, 31 : 506 - 512
  • [34] Using Machine Learning Techniques to Detect Defects in Images of Metal Structures
    Dementev, V. E.
    Suetin, M. N.
    Gaponova, M. A.
    PATTERN RECOGNITION AND IMAGE ANALYSIS, 2021, 31 (03) : 506 - 512
  • [35] Highly Applicable Iterative Network for 3-D Reconstruction Based on Multiview Satellite Images
    Hong, Zhonghua
    Yang, Peixin
    Pan, Haiyan
    Zhou, Ruyan
    Zhang, Yun
    Han, Yanling
    Wang, Jing
    Yang, Shuhu
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 10338 - 10351
  • [36] Exploring the use of satellite images in the estimation of potential malaria outbreak regions
    Marj, A. Ahmadian
    Mobasheri, M. R.
    Zoej, M. J. Valadan
    Rezaei, Y.
    Abaei, M. R.
    ENVIRONMENTAL HAZARDS-HUMAN AND POLICY DIMENSIONS, 2009, 8 (02): : 89 - 100
  • [37] Exploring Various Techniques for the Chemical and Biological Synthesis of Polymeric Nanoparticles
    Pulingam, Thiruchelvi
    Foroozandeh, Parisa
    Chuah, Jo-Ann
    Sudesh, Kumar
    NANOMATERIALS, 2022, 12 (03)
  • [38] A SURVEY ON VARIOUS IMAGE ENHANCEMENT TECHNIQUES FOR UNDERWATER ACOUSTIC IMAGES
    Sharumathi, K.
    Priyadharsini, R.
    2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 2016, : 2930 - 2933
  • [39] An Analysis of Various Edge Detection Techniques on Illuminant Variant Images
    Veni, S. H. Krishna
    Suresh, L. Padma
    ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY ALGORITHMS IN ENGINEERING SYSTEMS, VOL 2, 2015, 325 : 521 - 532
  • [40] PERFORMANCE ANALYSIS OF VARIOUS SEGMENTATION TECHNIQUES IN BREAST MAMMOGRAM IMAGES
    Sasikala, S.
    Ezhilarasi, M.
    Sudharsan, P.
    Sivakumari, C. L. Yashwanthi
    2014 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING APPLICATIONS (ICICA 2014), 2014, : 228 - 232