【摘要】 微波遥感数据具有穿透力强的特点,可在灾后复杂天气条件下快速获取灾区地表信息,如水体面积。但是在山区等复杂地形下,由于成像机理造成的阴影,会影响到水体提取的精度。为了快速有效地去除阴影,本文利用数据融合的方法,开展了基于光学影像辅助的微波遥感水体提取方法研究,并结合COSMO Skymed SAR数据和福卫-2号多光谱数据开展了实验分析。通过对HSV、Brovey、主成分、Gram-Schmidt四种融合方法效果的比较,发现无论从目视判读还是在定量指标上,Gram-Schmidt方法的效果都好于其他方法。将融合后图像进行监督分类,可以有效地区分出大部分雷达阴影和水体,从而快速有效地辅助在雷达图像上获取水体信息。更多还原

【Abstract】 Microwave remote sensing has stronger penetration,which make it possible to obtain land surface information under the complex weather condition.But radar shadow caused by mountains will affect the accuracy of water extraction.In order to quickly and effectively distinguish the shadow,this paper uses the method of image fusion to extract water based on microwave images and multi-spectral images by using the COSMO Skymed SAR and Formosat-2 multi-spectral satellite data.Comparing four fusion methods(HSV,PCA,Gram-Schmidt,Brovey) whether by visual interpretation or in terms of quantitative indicators,the result shows that Gram-Schmidt method is a better fusion method,which can distinguish water greatly from other surface features.This paper presents that most of radar shadow and water can be separated efficiently by supervised classification on the fused image,which can be an important auxiliary information to extract the water area from the SAR data.更多还原