In view of the fact that the traditional change detection algorithm mainly depends on the spectral information and fails to effectively use image feature detection advantage, the authors put forward a multi-feature fusion of remote sensing image change detection algorithm. First, color histogram and edge histogram of gradient image object with multi-scale segmentation is statistically analyzed based on the calculation of each object. Then, the object color distance and edge linear characteristics distance between different periods are calculated by using the earth mover’s distance method; the adaptive weighted method is used to combine color distance and edge linear characteristics distance so as to construct object heterogeneity. Finally, the images change detection results are analyzed by using histogram curvature. The experimental results show that the method can fully fuse the color and edge line features and improve the accuracy of detection.
Wei L F, Zhong Y F, Zhang L P , et al. Adaptive change method of remote sensing image fusion[J]. Journal of Remote Sensing, 2010,14(6):1196-1211.
[2]
Gong J Y, Sui H G, Sun K M , et al. Object-level change detection based on full-scale image segmentation and its application to Wenchuan Earthquake[J]. Science in China Series E:Technological Sciences, 2008,51(S2):110-122.
doi: 10.1007/s11431-008-6017-y
[3]
Zhang G, Li Y, Li Z J . A new approach toward object-based change detection[J]. Science China Technological Sciences, 2010,53(S1):105-110.
doi: 10.1007/s11431-010-3215-1
[4]
Bovolo F, Bruzzone L, Marconcini M . A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008,46(7):2070-2082.
doi: 10.1109/TGRS.2008.916643
Zhuang H F, Deng K Z, Fan H D . SAR images unsupervised change detection based on combination of texture feature vector with maximum entropy principle[J]. Acta Geodaetica et Cartographica Sinica, 2016,45(3):339-346.
Zhong J Q, Wang R S . Multitemporal remote sensing image change detection based on adaptive parameter estimation[J]. Acta Geodaetica et Cartographica Sinica, 2005,34(4):331-336.
Liu Z, Gong P, Shi P J , et al. Study on change detection automatically based on similarity calibration[J]. Journal of Remote Sensing, 2005,9(5):537-543.
Li L, Shu N, Wang K , et al. Change detection method for remote sensing images based on multi-features fusion[J]. Acta Geodaetica et Cartographica Sinica, 2014,43(9):945-953.
Wei L F, Wang H B . Change detection from high-resolution remote sensing image based on MSE model[J]. Spectroscopy and Spectral Analysis, 2013,33(3):728-732.
Wu Z C, Hu Z W, Zhang Q , et al. On combining spectral,textural and shape features for remote sensing image segmentation[J]. Acta Geodaetica et Cartographica Sinica, 2013,42(1):44-50.
Chen Y H, Feng T, Shi P J , et al. Classification of remot sensing image based on object oriented and class rules[J]. Geomatics and Information Science of Wuhan University, 2006,31(4):316-320.
Han H, Cao W, Gong T . The establishment of correlative degree in target re-identification based on earth mover’s distance[J]. Huazhong University of Science and Technology(Natural Science Edition), 2015,43(s1):435-439.
[16]
Wang A P, Wang S G, Lucieer A . Segmentation of multispectral high-resolution satellite imagery based on integrated feature distributions[J]. International Journal of Remote Sensing, 2010,31(6):1471-1483.
doi: 10.1080/01431160903475308
2018, Vol. 30 
), 李建磊2, 王华斌1, 杨化超2
