EMD与分形相结合的遥感影像水体信息提取方法

Water information extraction from remote sensing image using EMD and fraction method

提出一种基于经验模态分解(empirical mode decomposition,EMD)和分形理论相结合的遥感影像水体信息提取方法,该方法尝试结合影像的光谱特征和纹理特征以提高分类提取精度。对影像进行主成分分析得到有效信息量最大的第一主分量,计算每个像元的分维数得到分维图,同时将第一主分量EMD分解得到有效信息量较大的前3个经验模态函数,再结合原有的波段信息作为研究数据,利用极大似然法分类器提取水体信息。该方法充分结合了EMD在降噪和区分相似光谱特征中的优势和分形理论在纹理信息提取中的优势。研究表明,该方法可有效提高水体信息的提取精度,Kappa最高到0.932 5。

This paper presents a model for extracting water from remote sensing by using empirical mode decomposition（ EMD） and fractal theory. The authors tried to improve accuracy with spectral information and texture characteristics. Principal component analysis was carried out on the image to obtain the biggest first principal component that contains effective information, then the fractal dimension of each pixel was calculated;at the same time, the first principal component was decomposed with the method of EMD to get the first three empirical mode functions, which, coupled with the original band information, served as the research data. With the method of maximum likelihood classifier, the waters were extracted. This method fully combines the advantages of EMD method in noise reduction and the advantage of fractal theory in texture information extraction. Experiment shows that this method can effectively improve the extraction accuracy, with the Kappa up to 0. 932 5.