The traditional statistics-based change detection method requires the prerequisite that the dataset should obey the Gaussian distribution, such as the iterative chi-square test based change detection method. However, the dataset does not strictly obey the Gaussian distribution, and hence the result is not ideal. A novel change detection method is proposed in this paper, which does not need any assumptions and can take change detection by its own structure. First, an incremental segmentation method is adapted to get objects. After that, spectral and contextual features are combined to calculate its cosine value. Finally, changed objects are found by the box-plot. High-resolution remote sensing images of GF-1 are used as the experimental data. The results are much better than the results of the traditional statistical object-based change detection.
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