MPOLSAR-1.0:多维度SAR多波段全极化精细分类数据集

MPOLSAR-1.0:Multidimensional SAR Multiband Fully Polarized Fine Classification Dataset

地物精细分类是合成孔径雷达(SAR)的主要应用方向之一。在多波段全极化SAR工作模式下,可充分获取目标不同波段信息和极化响应特征,有望提高目标分类精度。然而国内外现有的数据集仅有个别波段、少数地区、少量样本的低分辨率全极化分类数据。为推动多波段全极化SAR分类应用的发展,在高分航空观测系统应用校飞与验证项目支持下,利用多维度SAR在海南的校飞数据构建了一个样本量充分大、地物类别较为丰富、分类可靠性较高的多波段全极化精细分类数据集。该文概述了该数据集的构成,给出了发布数据(MPOLSAR-1.0)的信息描述方式、数据集制作流程和方法,并分别基于极化特征分类方法和经典机器学习分类方法给出了初步的分类实验结果,为该数据集的共享和应用提供支撑。

Fine terrain classification is one of the main applications of Synthetic Aperture Radar(SAR).In the multiband fully polarized SAR operating mode,obtaining information on different frequency bands of the target and polarization response characteristics of a target is possible,which can improve target classification accuracy.However,the existing datasets at home and abroad only have low-resolution fully polarized classification data for individual bands,limited regions,and small samples.Thus,a multidimensional SAR dataset from Hainan is used to construct a multiband fully polarized fine classification dataset with ample sample size,diverse land cover categories,and high classification reliability.This dataset will promote the development of multiband fully polarized SAR classification applications,supported by the high-resolution aerial observation system application calibration and verification project.This paper provides an overview of the composition of the dataset,and describes the information and dataset production methods for the first batch of published data(MPOLSAR-1.0).Furthermore,this study presents the preliminary classification experimental results based on the polarization feature classification and classical machine learning classification methods,providing support for the sharing and application of the dataset.