融合对象级KPCA-DSFA的高分辨率遥感影像无监督变化检测OA

In order to address the challenges of incomplete identification and inaccurate judgment in land cover monitoring,while balancing integrity and practicality,a novel change detection method based on co-refinement of object-level fusion and graph cut with KPCA-DSFA was proposed,which used two regis-tered high-resolution remote sensing images.Firstly,relative radiometric correction and band stacking fu-sion were performed on the two-phase images.A simple non-iterative clustering algorithm was adopted for joint segmentation to generate homogeneous blocks that preserved the feature consistency of both image phases.Then the kernel PCA convolutional mapping network and deep slow feature analysis were coupled together for spatial-spectral feature extraction and deep semantic parsing respectively.Taking super pixels as the basic processing units,object-level high-dimensional spatial vectors were constructed via feature fu-sion to obtain change confidence information.Finally,an energy function model was established based on the Graph Cut,which leveraged the adjacency relationships and spatial differences of super pixel objects to achieve precise extraction of change regions through global optimization.Experimental results demonstrate that the proposed method achieves an overall accuracy of over 90%with excellent comprehensive perfor-mance.It can effectively suppress"salt-and-pepper"noise,significantly improve the recall rate of change regions,and exhibit favorable superiority and robustness.