Homogeneous Transformation Based on Deep-Level Features in Heterogeneous Remote Sensing Images

Homogeneous transformation receives considerable attention in recent years as it is essential for change detection in heterogeneous images. However, most existing methods perform the homogeneous transformation based on low-level features. It leads to inaccurate homogeneous representations of the heterogeneous images and accordingly causes unsatisfied performance of change detection. To solve this problem, this paper presents a new model that utilizes deep- level features for homogeneous transformation instead of low-level features. Experimental results on real remote sensing data show that, the proposed method achieves an overall change detection accuracy of 95.91%, providing better performance than the existing methods based on low- level features.

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