基于IHS变换与自适应区域特征的遥感图像融合算法

当前较多遥感图像融合算法主要通过独立像素点的像素特征来完成图像子带的融合,忽略了图像子带的区域相关性,导致融合图像存在不连续以及模糊效应等不足。因此,设计了IHS变换耦合自适应区域特征的遥感图像融合算法。引入IHS(intensity, hue, saturation)变换,对多光谱(MS)图像进行分解获取强度分量,将其与全色(PAN)图像进行融合。再通过非下采样Contourlet变换(NSCT)对PAN图像与强度分量进行子带分解,获取高、低频子带信息。并利用图像的区域能量以及区域空间特征,对低频子带融合模型的调节因子进行自适应整定,使得融合低频子带能够包含更多的空间信息。基于图像的区域方差特征来构建高频子带融合模型,使得融合高频子带能够包含更多的纹理信息。实验结果表明,与当前遥感图像融合算法相比,所提算法的融合图像具有更好地光谱特性以及空间特性。

Remote sensing image fusion algorithm based on IHS transform coupled with adaptive region features

At present, many remote sensing image fusion algorithms mainly use the pixel features of independent pixels to complete the fusion of image subbands, ignoring the regional correlation of image subbands, resulting in the fusion of image discontinuity and fuzzy effects. Therefore, a remote sensing image fusion algorithm based on IHS transform coupled with adaptive region features is proposed in this paper. The IHS transform is introduced to decompose the multispectral image to obtain the intensity component, and then fuse it with the panchromatic image. The non downsampling contourlet transform is used to decompose the PAN image and the intensity components to obtain the high and low frequency subband information. The adjustment factor of the low frequency subband fusion model is adaptively adjusted by using the regional energy and spatial characteristics of the image, so that the fusion subband can contain more spatial information. A high frequency subband fusion model is constructed based on the region variance feature of the image, which makes the fusion high frequency subband contain more texture information. Experimental results show that the proposed algorithm has better spectral and spatial characteristics than the current remote sensing image fusion algorithm.