基于第二代小波的超谱遥感图像融合算法研究

Research on Fusion of Hyperspectral Remote Sensing Image Based on Second Generation Wavelet

超谱遥感图像包含了大量的波段,波段之间的相关性较高,采用信息融合技术可以降低超谱图像的分析难度。提出了一种结构新颖的第二代小波加权融合算法。首先将图像分解为两个序列,用2阶Neville滤波器构造预测和更新算子,对两个序列以矩形栅格和梅花形栅格的格式进行交替预测和更新;再以各个波段的方差作为融合的特征,进行特征级第二代小波加权融合,最后对图像进行第二代小波重构。为了验证新方法的有效性,采用机载可见光红外成像光谱仪超谱遥感图像进行仿真,并与典型融合方法主成分分析和离散小波变换的融合效果相比较。实验结果表明提出的第二代小波加权融合算法能够很好地保持图像的空间特性和光谱特性,其熵值高于主成分分析融合结果0.1949,高于离散小波变换融合结果0.7998。

There are hundreds of bands in hyperspectral remote sensing image and the bands are highly correlative, and the difficulty of analysis can be reduced by using fusion technology. A novel structure of second generation wavelet weighting fusion algorithm is proposed. Firstly, image is decomposed into two serials, which are predicted and updated by two-order Neville filter on rectangle and quincunx grid by turns. Secondly, feature level second generation wavelet fusion is carried on the updated serial by using the variance of each band as the feature of fusion. Finally the image is reconstructed by reverse second generation wavelet. In order to test the effect of the new method, hyperspectral remote sensing image of airborne visible and infrared imaging spectrometer is simulated on Pentium IV computer. Compared with typical fusion method such as principal component analysis and discrete wavelet transform, the result of the experiment shows that the proposed method can retain spatial and spectral feature, the entropy is bigger than principal component analysis 0.1949, and bigger than discrete wavelet transform 0.7998.