Forest Disaster Detection Method Based on Ensemble Spatial-Spectral Genetic Algorithm

被引:0
|
作者
Cao, Yang [1 ]
Feng, Wei [1 ]
Quan, Yinghui [1 ]
Bao, Wenxing [2 ]
Dauphin, Gabriel [3 ]
Ren, Aifeng [1 ]
Yuan, Xiaoguang [1 ]
Xing, Mengdao [4 ]
机构
[1] Xidian University, Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xi'an,710071, China
[2] North Minzu University, School of Computer Science and Engineering, Yinchuan,750021, China
[3] University Paris Xiii, Laboratory of Information Processing and Transmission, Institut Galilée, Paris,93430, France
[4] Xidian University, Academy of Advanced Interdisciplinary Research, Xi'an,710071, China
关键词
Classification (of information) - Clustering algorithms - Copying - Damage detection - Disasters - Forestry - Fuzzy clustering - Genetic algorithms - Information filtering - Linear programming - Photomapping - Pixels - Signal detection - Space optics - Vegetation mapping;
D O I
暂无
中图分类号
学科分类号
摘要
Remote sensing image change detection is the key technology for monitoring forest windfall damages. A genetic algorithm (GA) is a branch of intelligent optimization techniques available to contribute to the surveys of windstorm and wildfire detection in forest areas. However, traditional GAs remain challenging due to several issues, such as complex calculation, poor noise immunity, and slow convergence. Analysis at the spatial level allows classifications to utilize the contextual and hierarchical information of image objects in addition to solely using spectral information. In addition, ensemble learning presents a possibility for improving classification accuracy. Ensemble classifiers combined with the spatial-based GA offers a promising method [ensemble spatial-spectral genetic algorithm (E-nGA)] for automating the process of monitoring forest loss. The research in this article is presented in four parts. First, block-matching and 3-D filtering is performed to suppress noises while enhancing valuable information. The difference image is, then, generated using the image difference method. Afterward, context-based saliency detection and fuzzy c-means algorithm are conducted on the difference image to reduce the search space. Finally, the proposed E-nGA is executed to further classify the pixels and produce the final change map. Our first proposition is to design improved genetic operators in the GA, relying not only on pixel values but also on spatial information. Our second proposition is to consider an ensemble classification model based on multiple vegetation features for decision integration. Six frequently used classification methods, as well as the simple GA, are executed to demonstrate the effectiveness of the proposed framework in improving the robustness and detection accuracy. © 2008-2012 IEEE.
引用
收藏
页码:7375 / 7390
相关论文
共 50 条
  • [1] Forest Disaster Detection Method Based on Ensemble Spatial-Spectral Genetic Algorithm
    Cao, Yang
    Feng, Wei
    Quan, Yinghui
    Bao, Wenxing
    Dauphin, Gabriel
    Ren, Aifeng
    Yuan, Xiaoguang
    Xing, Mengdao
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2022, 15 : 7375 - 7390
  • [2] Evaluation of a change detection method based on joint spatial-spectral information
    Izquierdo, EM
    Martín, CG
    Hidalgo, AA
    Saavedra, ML
    [J]. REMOTE SENSING IN TRANSITION, 2004, : 121 - 126
  • [3] A NOVEL SPATIAL-SPECTRAL RANDOM FOREST ALGORITHM FOR PINE WILT MONITORING
    Zhang, Yali
    Feng, Wei
    Quan, Yinghui
    Zhong, Xian
    Song, Yijia
    Li, Qiang
    Dauphin, Gabriel
    Wang, Yong
    Xing, Mengdao
    [J]. 2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 6045 - 6048
  • [4] Improved Collaborative Algorithm Based on Spatial-spectral Joint Clustering for Hyperspectral Anomaly Detection
    Ma Shi-xin
    Liu Chun-tong
    Li Hong-cai
    He Zhen-xin
    Wang Hao
    [J]. ACTA PHOTONICA SINICA, 2019, 48 (01)
  • [5] A HYPERSPECTRAL SPATIAL-SPECTRAL ENHANCEMENT ALGORITHM
    Yi, Chen
    Zhao, Yongqiang
    Yang, Jingxiang
    [J]. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 7228 - 7231
  • [6] LOGO DETECTION BASED ON SPATIAL-SPECTRAL SALIENCY AND PARTIAL SPATIAL CONTEXT
    Gao, Ke
    Lin, Shouxun
    Zhang, Yongdong
    Tang, Sheng
    Zhang, Dongming
    [J]. ICME: 2009 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-3, 2009, : 322 - 329
  • [7] Hyperspectral anomaly detection based on spatial-spectral multichannel autoencoders
    Jia, Sen
    Liu, Kuan
    Xu, Meng
    Zhu, Jiasong
    [J]. National Remote Sensing Bulletin, 2024, 28 (01) : 55 - 68
  • [8] A CLASSIFICATION METHOD WITH A SPATIAL-SPECTRAL VARIABILITY
    ARAI, K
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 1993, 14 (04) : 699 - 709
  • [9] Cloud Detection Method Based on Spatial-Spectral Features and Encoder-Decoder Feature Fusion
    Zhang, Jing
    Shi, Xinlong
    Wu, Jun
    Song, Liangnong
    Li, Yunsong
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61 : 1 - 15
  • [10] RGB-D Visual Saliency Detection Method Based on Spatial-Spectral Mixture Analysis
    [J]. Yuan, Xia (yuanxia@njust.edu.cn), 1600, Chinese Academy of Sciences (39):