基于Freeman散射熵和各向异性度的极化SAR影像分类算法研究

摘要

The unsupervised classification of preserving polarimetric scattering characteristics is a classic classification method. But this method cannot classify the different objects with similar main scattering mechanism powers, especialry for shadow, water and bare soil, which have very low backscattering powers. So the entropy and anisotropy parameters based on Freeman three-component decomposition is introduced, and applied into polarimetric SAR classification. Before applying the decomposition, a polarimetric orientation compensation （P~C） procedure is performed for a better result. And then, the entropyH~and anisotropyAt are calculated after Freeman decomposition. Through choosing appropriate values ofHf andAf , the shadow and water can be extracted out. The other pixels are then divided into three categories by their dominant scattering mechanisms. Each category is divided into25-100 classes by the Hf-Af plane to preserve the purity of scattering characteristics, and merged into specified number of classes by Wishart distance measure. At last pixels in each category are iteratively classified by the Wishart classifier independently, A Radarsat-2 C band polarimetric SAR image was used to illustrate the effectiveness of the proposed method.%极化SAR影像中阴影、水体和裸露的耕地3种地物类型有非常相似的极化散射特性，常规基于非相干分解的分类方法难以将其有效地区分。对此，本文引入基于Freeman分解的散射熵Hf和各向异性度Af两个特征参数，并将其用于极化SAR影像分类。首先利用Hf和Af参数将阴影和水体提取出来，然后将其他地物按散射机制分为3大类，并对每一类再次利用Hf和A，参数进行细分，最后通过基于wshart分布的聚类和迭代分类，得到最终的分类结果。通过利用Radarsat-2在河南登封获取的全极化SAR数据进行试验，表明该算法执行效率高，能够有效地区分阴影、水体和裸露的耕地，并且对其他地物类型也有很好的分类效果。