摘要
该文描述了一种对多波段、全极化AIRSAR图像中的地物目标进行分类的最大似然(ML)分类算法。该算法的特点是利用极化SAR图像的最优状态进行分类。本文描述了最优状态的搜索算法和地貌分类算法,并利用美国AIRSAR获得的多波段(P,L和C)、全极化图像数据对本算法进行检验。与利用单波段、单极化图像数据得到的分类结果相比,本文提出的基于最优状态的分类算法可以显著地提高分类精度。
An ML Classification algorithm that classifies the terrain object in the multi-band, full-polarization SAR image is described in this paper. Its main feature is that the optimal state of polarization SAR image is utilized to classify objects. The searching algorithm for the optimal state and the classification algorithm of terrain targets are provided, and the classifier's performance is verified using the multi-band (P, L and C band), full-polarization testing data that is acquired by AIRSAR. Compared with the single band, single polarization SAR data, the classification accuracy of the optimal state based classification algorithm is improved significantly.
出处
《电子与信息学报》
EI
CSCD
北大核心
2001年第5期507-511,共5页
Journal of Electronics & Information Technology
基金
微波成像国防重点实验室资助项目
