全极化SAR数据在地表覆盖/利用监测中的应用

SIR-C/X-SAR是运行在地球轨道上的第一个多波段(L、C、X)全极化(HH、VV、VH和HV)成像雷达系统,该系统具有极化测量和干涉测量功能。全极化雷达测量每一个像元的全散射矩阵,所获取的信息非常丰富。但是,由于这些极化合成图像具有较高的相关性,导致了图像信息提取精度的降低。本文基于新疆和田地区的SIR-CL波段全极化雷达数据,利用全散射矩阵的特点合成了HH-VV极化相关图像、极化度图像、目标增强图像和相位差图像。这些图像相关性小,地表覆盖信息丰富,提高了全极化SAR数据在实验区信息提取的准确度。

THE APPLICATION OF QUAD-POLARIZATION SAR DATA TO THE MONITORING OF LANDUSE AND COVER BASED ON TARGET DECOMPOSITION

SIR-C is the first space-borne imaging radar system with multi-wavelength and quad-polarization developed by the joint effort of the USA, Italy and Germany. Polarization SAR measures the scattering matrix of each pixel on ground and synthesizes the image at given orientation and ellipticity angle, including linear and elliptical polarization. It has many advantages over single or multi-polarization SAR in such aspects as detecting objects, identifying targets and extracting texture. Nevertheless, the relatively high correlation of the synthesized polarized images and the complexness of scattering of objects often lead to wrong interpretation of the images and poor accuracy of classification. Based on SIR-C data of Hetian prefecture in Xinjiang, the authors used the target decomposition theory to decompose the data into three non-relevant scattering components. The result shows that the decomposed three scattering components reflect the correct scattering feature. The authors then combined them with polarimetric synthesized SAR power image to classify the experimental area by using MLC or neural net. The classification result shows that the method can effectively extract the information of land cover, achieve relatively good classification accuracy of ground objects and improve the capability of SAR for monitoring the land use and cover.