利用SFIM、MLT、HPF和修改的Brovey(MB)等遥感影像融合算法对Landsat 7 ETM 影像进行融合和自动分类研究，并就融合影像的光谱保真度、高频空间信息融人度和分类精度对这些方法进行评价。结果表明SFIM变换几乎完全保持了原始影像的光谱特点，并具有最高的平均分类精度；MB变换具有最高的高频空间信息融人度；MLT变换也具有较高的分类精度；只有HPF变换的各项指标都不突出。所有4种融合影像的分类精度都较原始影像的分类精度有明显的提高。这表明，源于同一传感器系统的不同分辨率影像的融合可以避免异源传感器融合影像所常见的各种参数、时相和配准误差，所以能够明显地提高影像的自动分类精度。

Fusion of images with different spatial resolution can improve visualization of the images involved. This study tries to show that the fusion of the images from the same sensor system can also improve classification accuracy of the images. Four image fusion algorithms have been employed in the study of data fusion and classification of Landsat 7 ETM imagery, taking southeastern part of Fuzhou City as the study area. These are the Smoothing Filter-Based Intensity Modulation (SFIM), Modified Brovey (MB) Transform, Multiplication (MLT) Transform, and High-Pass Filter (HPF) Transform. The effectiveness of the four fusion algorithms has been evaluated based on spectral fidelity, high spatial frequency information gain, and classification accuracy. The study reveals that the SFIM transform is the best method in retaining spectral information of original image, which does not cause spectral distortion, and achieving the highest classification accuracy. MB-fused image has highest spatial frequency information gain but significantly loses spectral properties of the original image. The study shows all four fusion algorithms used can significantly improve the classification accuracy of the fused imagery. Therefore, fused images from the same sensor system can be used for improving not only visual interpretation but also classification accuracy due to free of the seasonal difference, various solar illumination and other environmental condition differences, and co-registration errors, which are common to the fusion using images from different sensor systems.