Multi-feature fusion-based recognition and processing of impulse noise in remote sensing images
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摘要: 消除脉冲噪声,获取高质量的遥感图像对应用研究有着重要意义。消除高密度脉冲噪声的同时,保持原有遥感图像的边缘细节信息一直是这一领域中的难题。该文认为被脉冲噪声冲击后的图像会出现不确定性突变,为了解决这种不确定性问题,基于证据理论,利用脉冲噪声的多个特征进行了不确定性建模; 融合了BJS散度和信度熵,给出新的权重分配,得到了新的概率指派; 再根据融合规则和概率转换,给出噪声与信号点的分类依据,从而有效降低了高度冲突发生的可能性。实验结果表明,在噪声密度达到90%以上时,该文提出的方法仍然有效,且在消噪后的遥感图像中对不同地物信息的细节保持良好。Abstract: Eliminating impulse noise of high-quality remote sensing images is of great significance for applied research. It has always been a challenge to eliminate high-density impulse noise while remaining detailed information on edges in original remote sensing images. This study concluded that uncertain changes will appear when a remote sensing image is corrupted by impulse noise. Given this, an uncertainty model based on the evidence theory was constructed using multiple features of impulse noise. The BJS divergence and the reliability entropy were fused into the model to obtain new weights and a new probability assignment. Then, the classification between noise and signals was given according to fusion rules and probability transformation, thus effectively reducing the possibility of high-level conflicts. The experimental results show that the classification method proposed in this study is effective even when the noise density is up to over 90% and can well maintain detailed information on different ground objects in the denoised remote sensing images.
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Key words:
- evidence theory /
- uncertainty modeling /
- fusion rules /
- highly conflict /
- remote sensing image
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