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Multi-channel Satellite Cloud Image Fusion in the Shearlet Transform Domain and Its Influence on Typhoon Center Location

机译:Shearlet变换域中的多通道卫星云图像融合及其对台风中心位置的影响

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A multi-channel satellite cloud image fusion method by the shearlet transform is proposed. The Laplacian pyramid algorithm is used to decompose the low frequency sub-images in the shearlet domain. It averages the values on its top layer, and takes the maximum absolute values on the other layers. In the high frequency sub-images of the shearlet domain, fusion rule is constructed by using information entropy, average gradient and standard deviation. Next, a nonlinear operation is performed to enhance the details of the fusion high frequency sub-images. The proposed image fusion algorithm is compared with five similar image fusion algorithms: the classical discrete orthogonal wavelet, curvelet, NSCT, tetrolet and shearlet. The information entropy, average gradient and standard deviation are used objectively evaluate the quality of the fused images. In order to verify the efficiency of the proposed algorithm, the fusion cloud image is used to determine the center location of eye and non-eye typhoons. The experimental results show that the fused image obtained by proposed algorithm improve the precision of determining the typhoon center. The comprehensive performance of the proposed algorithm is superior to similar image fusion algorithms.
机译:提出了一种基于剪切波变换的多通道卫星云图像融合方法。拉普拉斯金字塔算法用于分解小波域中的低频子图像。它对顶层的值取平均值,并在其他层取最大绝对值。在小波域的高频子图像中,利用信息熵,平均梯度和标准差构造融合规则。接下来,执行非线性操作以增强融合高频子图像的细节。将提出的图像融合算法与五种相似的图像融合算法进行了比较:经典离散正交小波,curvelet,NSCT,tetrolet和shletlet。信息熵,平均梯度和标准偏差用于客观地评估融合图像的质量。为了验证所提算法的有效性,融合云图像用于确定台风和非台风的中心位置。实验结果表明,所提算法获得的融合图像提高了台风中心的确定精度。该算法的综合性能优于同类图像融合算法。

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