Acta Geodaetica et Cartographica Sinica ›› 2017, Vol. 46 ›› Issue (9): 1147-1155.doi: 10.11947/j.AGCS.2017.20160606

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Remote Sensing Image Change Detection Based on the Combination of Pixel-level and Object-level Analysis

FENG Wenqing, SUI Haigang, TU Jihui, SUN Kaimin   

  1. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
  • Received:2016-11-28 Revised:2017-05-24 Online:2017-09-20 Published:2017-10-12
  • Supported by:
    The National Key Research and Development Program of China (No. 2016YFB0502600);The Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (No. 16E01);The National Natural Science Foundation of China(No. 41471354)

Abstract: In order to improve the change detection accuracy of the high resolution remote sensing image, a novel framework based on the combination of pixel-level and object-level analysis is proposed. Firstly, the two temporal images are superimposed, and the principal component analysis is performed. Then, it is utilized that the entropy rate segmentation algorithm to segment the first principal component image by changing the number of super-pixels to obtain the multi-layer super-pixel regions with different sizes. At the same time, by analyzing the difference of spectral feature and texture feature on two temporal images, it is used that adaptive PCNN neural network algorithm to make a fusion of the two difference images. Afterwards, the level set (CV) method is used to get the pixel-level change detection results. At last, the change intensity level quantization and decision level fusion are used on the initial change detection results with the region labeling matrix, serving as the post-processing part to obtain the changed objects. Experimental results on the sets of SPOT-5 multi-spectral images show that the new framework can effectively integrate the advantages of pixel-based and object-based image analysis methods, which can further improve the stability and applicability of the change detection process.

Key words: pixel-level, object-level, change detection, super-pixel, PCNN neural network, decision level fusion

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