ANALYSIS OF THE TARGET DECOMPOSITION TECHNIQUE ATTRIBUTES AND POLARIMETRIC RATIOS TO DISCRIMINATE LAND USE AND LAND COVER CLASSES OF THE TAPAJÓS REGION

Abstract This study aims to analyze the capability of the target decomposition techniques and the polarimetric ratios applied to the ALOS/PALSAR-2 satellite polarimetric images to discriminate the land use and land cover classes in the Tapajós National Forest region, Pará State. Three full polarimetric ALOS/PALSAR-2, level 1 single look complex scenes were selected to generate the coherence and the covariance matrices to derive the Cloude-Pottier and the Freeman-Durden target decomposition attributes. From the radiometrically calibrated PALSAR-2 images, we generated the backscatter coefficients, the cross polarized ratio (RC; HV/HH), the parallel polarized ratio (RP; VV/HH) and the Radar Forest Degradation Index (RFDI). The images resulting from these polarimetric attributes were processed by the Maximum Likelihood (MAXVER) classifier coupled with the Iterated Conditional Modes (ICM) contextual algorithm. We found that the classifications derived from the target decomposition attributes, mainly from the Cloude-Pottier technique, with a Kappa index of 0.75, presented a significant higher performance than those derived from the RC ratio, RP ratio, and RFDI.

摘要 本研究旨在分析目标分解技术与极化比值法应用于ALOS/PALSAR-2卫星极化影像的能力，以区分帕拉州塔帕若斯国家林区的土地利用与土地覆盖类型。本研究选取3景全极化ALOS/PALSAR-2一级单视复数影像，通过生成相干矩阵与协方差矩阵，提取Cloude-Pottier目标分解（Cloude-Pottier target decomposition）与Freeman-Durden目标分解（Freeman-Durden target decomposition）属性。从经过辐射定标的PALSAR-2影像中，本研究生成了后向散射系数、交叉极化比值（RC；HV/HH）、同向极化比值（RP；VV/HH）以及雷达森林退化指数（Radar Forest Degradation Index, RFDI）。基于上述极化属性生成的影像，采用最大似然（MAXVER）分类器结合迭代条件模式（Iterated Conditional Modes, ICM）上下文算法进行处理。研究结果表明，基于目标分解属性（主要为Cloude-Pottier技术）得到的分类结果卡帕指数达0.75，其分类性能显著优于基于RC比值、RP比值与RFDI得到的分类结果。