关键词: 影像融合, 合成孔径雷达（SAR）, 地表覆被分类, 面向对象分类方法, 多云雾地区

Abstract: ： This research aimed to improve the utilization ratio of optical remote sensing image in cloudy areas and explore the best fusion scales of different cloud cover optical remote sensing image and SAR image. Taking the Yuzhong area of Chongqing city as the research area, wavelet transform fusion, multiplicative transform fusion and high-pass filter fusion was carried out based on sentinel-1A polarized synthetic aperture radar （SAR） image and different cloud cover of sentinel-2A （cloud volume of 0, 10%, 20%, 30%） multi-spectral images, then the effect of image fusion was evaluated by image evaluation method, finally all images were classified by object-oriented methods,and the final classification accuracy was compared by the confusion matrix. The results show that, in the cloudless case, the wavelet transform has the best fusion effect, and the brightness and contrast of the original multi-spectral image are preserved to the maximum extent, which effectively prevents the loss of image information, and the interpretation ability of the vegetation is obviously improved, but after the fusion the image fidelity is poor, and the other two fusion effects are relatively inferior. Therefore, when multi-source remote sensing data fusion is performed in a cloudy fog region, especially when heterogeneous data is fused, the wavelet fusion algorithm is preferred. When the cloud cover is more than 10%, although the information entropy is slightly increased in the four fusion algorithms, the average gradient and standard deviation are reduced, which makes the interpretation difficult, resulting in the final classification accuracy is slightly lower than the sentinel-2A image, much lower than sentinel-1A images basically cannot meet the requirements for use. Therefore, when performing surface overlay interpretation, it is recommended to replace the optical images with SAR images.

Key words: image fusion, polarimetric synthetic aperture radar（sar）, land cover classification, object-oriented classification, cloudy areas