7 February 2017 Object-based change detection method using refined Markov random field
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J. of Applied Remote Sensing, 11(1), 016024 (2017). doi:10.1117/1.JRS.11.016024
In order to fully consider the local spatial constraints between neighboring objects in object-based change detection (OBCD), an OBCD approach is presented by introducing a refined Markov random field (MRF). First, two periods of images are stacked and segmented to produce image objects. Second, object spectral and textual histogram features are extracted and G-statistic is implemented to measure the distance among different histogram distributions. Meanwhile, object heterogeneity is calculated by combining spectral and textual histogram distance using adaptive weight. Third, an expectation-maximization algorithm is applied for determining the change category of each object and the initial change map is then generated. Finally, a refined change map is produced by employing the proposed refined object-based MRF method. Three experiments were conducted and compared with some state-of-the-art unsupervised OBCD methods to evaluate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method obtains the highest accuracy among the methods used in this paper, which confirms its validness and effectiveness in OBCD.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Daifeng Peng, Yongjun Zhang, "Object-based change detection method using refined Markov random field," Journal of Applied Remote Sensing 11(1), 016024 (7 February 2017). https://doi.org/10.1117/1.JRS.11.016024 Submission: Received 20 September 2016; Accepted 19 January 2017
Submission: Received 20 September 2016; Accepted 19 January 2017

Image segmentation

Magnetorheological finishing

Expectation maximization algorithms

Data modeling

Distance measurement

Feature extraction

Remote sensing

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