Journal of Geo-information Science ›› 2018, Vol. 20 ›› Issue (8): 1150-1159.doi: 10.12082/dqxxkx.2018.170537

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Change Detection Method for High Resolution Remote Sensing Images Based on BOW Features Representation

LUO Xing1,2,3(), XU Weiming1,2,3,*(), WANG Jia1,2,3   

  1. 1. National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou 350116, China
    2. Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou 350116, China;
    3. Spatial Information Engineering Research Centre of Fujian Province, Fuzhou 350116, China
  • Received:2017-11-17 Revised:2018-04-16 Online:2018-08-25 Published:2018-08-24
  • Contact: XU Weiming;
  • Supported by:
    Science and Technology Agency of Fujian Province, No.2017Y0055;Haixi Government Affairs Big Data Collaborative Innovation Center;“Digital Fujian” Program, No.2016-203.


A novel change detection method is proposed based on BOW (Bag of Words) features of the image objects, aiming at the drawbacks of traditional change detection methods and the poor interpretation capacity of basic features. First of all, a simultaneous segmentation method which took both spectral features and geometric information into consideration was applied to two images that were previously preprocessed and layer-stacked to acquire corresponding image objects; then mathematical mean and variance of every image band, and six textures from its panchromatic image were extracted as basic features. Moreover, the BOW representation of each image object was constructed by regarding objects as documents and basic features of pixels as words. Finally, similarity measurement was used to compare BOW features to define changed and no-changed areas. In this paper, experimental results on two sets of WorldView-2 images showed that the proposed method demonstrated superiority in completeness of detection result and outperformed the methods for comparison in accuracy assessment. In all, the proposed method can basically meet the needs of change detection work and provide an effective way for data mining and analysis of high resolution remote sensing images.

Key words: bag-of-visual-words, change detection, mid-features, objects, high resolution remote sensing images