Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Laser & Optoelectronics Progress, Volume. 56, Issue 12, 121003(2019)

Remote Sensing Image Change Detection Based on Density Attraction and Multi-Scale and Multi-Feature Fusion

Qiuhan Jin1,2、*, Yangping Wang1,2、**, and Jingyu Yang1,2、***
Author Affiliations
  • 1 School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China
  • 2 Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China;
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
    Equations (0)
    References (19)
    Cited By (1)
    Tools
    Paper Information
    Topics
    Figures & Tables(11)
    Flow chart of proposed algorithm

    Fig. 1. Flow chart of proposed algorithm

    Download full size
    Schematic of Gabor filter

    Fig. 2. Schematic of Gabor filter

    Download full size
    Schematic of neighborhood system and distance. (a) Neighborhood system centered at (i, j); (b) distance between center pixel (i, j) and its neighborhood

    Fig. 3. Schematic of neighborhood system and distance. (a) Neighborhood system centered at (i, j); (b) distance between center pixel (i, j) and its neighborhood

    Download full size
    Remote sensing image data and reference change map in first set of experiments. (a) 2014; (b) 2018; (c) reference change map

    Fig. 4. Remote sensing image data and reference change map in first set of experiments. (a) 2014; (b) 2018; (c) reference change map

    Download full size
    Remote sensing image data and reference change map for second set of experiments. (a) 2016; (b) 2018; (c) reference change map

    Fig. 5. Remote sensing image data and reference change map for second set of experiments. (a) 2016; (b) 2018; (c) reference change map

    Download full size
    Results of change detection under different weights and classification methods for first set of experiments. (a) WS=0, WT=1, MRF; (b) WS=0.4, WT=0.6, MRF; (c) WS=0.6, WT=0.4, MRF; (d) WS=1, WT=0, MRF; (e) adaptive weight, MRF; (f) adaptive weight, DAMRF

    Fig. 6. Results of change detection under different weights and classification methods for first set of experiments. (a) WS=0, WT=1, MRF; (b) WS=0.4, WT=0.6, MRF; (c) WS=0.6, WT=0.4, MRF; (d) WS=1, WT=0, MRF; (e) adaptive weight, MRF; (f) adaptive weight, DAMRF

    Download full size
    Results of change detection under different weights and classification methods for second set of experiments. (a) WS=0, WT=1, MRF; (b) WS=0.4, WT=0.6, MRF; (c) WS=0.6, WT=0.4, MRF; (d) WS=1, WT=0, MRF; (e) adaptive weight, MRF; (f) adaptive weight, DAMRF

    Fig. 7. Results of change detection under different weights and classification methods for second set of experiments. (a) WS=0, WT=1, MRF; (b) WS=0.4, WT=0.6, MRF; (c) WS=0.6, WT=0.4, MRF; (d) WS=1, WT=0, MRF; (e) adaptive weight, MRF; (f) adaptive weight, DAMRF

    Download full size
    Continuous graph of detection accuracy for first set of experiments

    Fig. 8. Continuous graph of detection accuracy for first set of experiments

    Download full size
    Continuous graph of detection accuracy for second set of experiments

    Fig. 9. Continuous graph of detection accuracy for second set of experiments

    Download full size

    • Table 1. Quantitative evaluation of change detection results for Fig. 6

      View table

      Table 1. Quantitative evaluation of change detection results for Fig. 6

      MethodandparameterFalsealarm ratePF /%Misseddetectionrate PM /%OverallaccuracyPT /%
      WS=0,WT=1, MRF13.133.289.3
      WS=0.4,WT=0.6, MRF16.628.389.2
      WS=0.6,WT=0.4, MRF16.327.589.5
      WS=1,WT=0, MRF22.429.287.6
      Adaptive weight,MRF15.926.489.7
      Adaptive weight,DAMRF14.823.390.8

    • Table 2. Quantitative evaluation of change detection results for Fig. 7

      View table

      Table 2. Quantitative evaluation of change detection results for Fig. 7

      MethodandparameterFalsealarm ratePF /%Misseddetectionrate PM /%OverallaccuracyPT /%
      WS=0,WT=1, MRF45.534.684.2
      WS=0.4,WT=0.6, MRF48.623.483.3
      WS=0.6,WT=0.4, MRF41.720.586.8
      WS=1,WT=0, MRF56.730.279.7
      Adaptive weight,MRF36.216.788.3
      Adaptive weight,DAMRF34.813.889.6
    Tools

    Get Citation

    Copy Citation Text

    Qiuhan Jin, Yangping Wang, Jingyu Yang. Remote Sensing Image Change Detection Based on Density Attraction and Multi-Scale and Multi-Feature Fusion[J]. Laser & Optoelectronics Progress, 2019, 56(12): 121003

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Image Processing

    Received: Nov. 26, 2018

    Accepted: Jan. 11, 2019

    Published Online: Jun. 13, 2019

    The Author Email: Jin Qiuhan (18298378747@163.com), Wang Yangping (wangyp@mail.lzjtu.cn), Yang Jingyu (yangjy@mail.lzjtu.cn)

    DOI:10.3788/LOP56.121003

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology