Forest Region Extraction and Evaluation from Satellite Images using CNN Segmentation

被引:0
|
作者
Ramadasan, Swaetha [1 ]
Vijayakumar, K. [2 ]
Prabha, S. [3 ]
Karthickeien, E. [4 ]
机构
[1] Perma Technol, Atlanta, GA 30342 USA
[2] St Josephs Inst Technol, Dept Informat Technol, Chennai 600119, Tamil Nadu, India
[3] SIMATS, Saveetha Sch Engn, Dept CSE, Ctr Res & Innovat, Chennai 602105, Tamil Nadu, India
[4] Amrita Vishwa Vidyapeetham, Comp & Commun Engn, Chennai 601103, Tamil Nadu, India
关键词
Forest region; Satellite Image; VGG-UNet; Segmentation; Verification; LAND-COVER CHANGE;
D O I
10.1109/ACCAI61061.2024.10602190
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The segmentation and analysis of forests from satellite images play a vital role in the comprehensive monitoring and effective management of ecosystems. This process facilitates precise evaluations of various critical aspects such as forest cover, biodiversity, and overall health. By connecting this information, resource planning becomes more informed and strategic, contributing to sustainable forest management practices. Moreover, the data derived from satellite imagery aids in identifying areas susceptible to deforestation, allowing for timely intervention to mitigate its adverse environmental impacts. The integration of advanced satellite technologies in forest analysis enhances the ability to address contemporary environmental challenges, providing a foundation for policies and practices that promote ecological resilience and the long-term well-being of our planet. In this work, VGG-UNet is implemented to segment the forest and it achieved an accuracy of >94%, which is better compared to other existing methods in the literature.
引用
收藏
页数:5
相关论文
共 50 条
  • [41] Road segmentation from satellite images using FCNN for autonomous driving vehicles
    Anjitha, A. P.
    Saritha, M.
    Baburaj, M.
    2022 IEEE 19TH INDIA COUNCIL INTERNATIONAL CONFERENCE, INDICON, 2022,
  • [42] Segmentation of aerial images and satellite images using unsupervised nonlinear approach
    Ye, Zhengmao
    Luo, Jiecai
    Bhattacharya, Pradeep
    Ye, Yongmao
    WSEAS Transactions on Systems, 2006, 5 (02): : 333 - 339
  • [43] HSV Color Space Based Segmentation of Region of Interest in Satellite Images
    Ganesan, P.
    Rajini, V.
    Sathish, B. S.
    Shaik, Khamar Basha.
    2014 INTERNATIONAL CONFERENCE ON CONTROL, INSTRUMENTATION, COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICCICCT), 2014, : 101 - 105
  • [44] Heart Region Segmentation using Dense VNet from Multimodality Images
    Kanakatte, Aparna
    Bhatia, Divya
    Ghose, Avik
    2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), 2021, : 3255 - 3258
  • [45] SOM based segmentation method for water region detection in satellite images
    Tiagrajah, V. J.
    Win, Kong
    WORLD JOURNAL OF ENGINEERING, 2013, 10 (01) : 95 - 99
  • [46] Using Deep Networks for Semantic Segmentation of Satellite Images
    Selea, Teodora
    Neagul, Marian
    2017 19TH INTERNATIONAL SYMPOSIUM ON SYMBOLIC AND NUMERIC ALGORITHMS FOR SCIENTIFIC COMPUTING (SYNASC 2017), 2017, : 409 - 415
  • [47] Segmentation based traversing-agent approach for road width extraction from satellite images using volunteered geographic information
    Manandhar, Prajowal
    Marpu, Prashanth Reddy
    Aung, Zeyar
    APPLIED COMPUTING AND INFORMATICS, 2021, 17 (01) : 131 - 152
  • [48] Region extraction from multiple images
    Ishikawa, H
    Jermyn, IH
    EIGHTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOL I, PROCEEDINGS, 2001, : 509 - 516
  • [49] Satellite Remote Sensing Images of Crown Segmentation and Forest Inventory Based on BlendMask
    Ji, Zicheng
    Xu, Jie
    Yan, Lingxiao
    Ma, Jiayi
    Chen, Baozhe
    Zhang, Yanfeng
    Zhang, Li
    Wang, Pei
    FORESTS, 2024, 15 (08):
  • [50] A Color and Multispectral Fractal Model for Forest Region Identification in Satellite Images
    Coliban, Radu-Mihai
    Radoi, Anamaria
    Ivanovici, Mihai
    2016 INTERNATIONAL CONFERENCE ON COMMUNICATIONS (COMM 2016), 2016, : 381 - 384