电子学报2017，Vol.45Issue(12)：2855-2862,8.DOI:10.3969/j.issn.0372-2112.2017.12.005
结合分类与迁移学习的薄云覆盖遥感图像地物信息恢复
Ground Object Information Recovery for Thin Cloud Contaminated Remote Sensing Images by Combining Classification with Transfer Learning
摘要
Abstract
By using multi-source and multi-temporal remote sensing images,a ground object information recovery algorithm for thin cloud contaminated remote sensing images is proposed by combining classification with transfer learning.Firstly,multi-resolution decomposition of multi-source and multi-temporal remote sensing images is performed by using multi-directional nonsubsampled dual-tree complex wavelet transform.The decomposed high frequency coefficients of the ground objects of the thin cloud images are primarily classified by using Bayesian method.Then the transfer least square support vector regression model is trained to obtain the model parameters by using the domain adaptive learning of the low frequency coefficients of each class of ground objects.Finally,the low frequency coefficients of the thin cloud-contaminated images are predicted by using those of the cloudless reference images.The thin clouds are removed and the ground object information of the thin cloud contaminated images is recovered.Experimental results show that the ground objects recovered by the proposed algorithm have clear spatial details and small spectral distortion.Especially for the thin cloud contaminated remote sensing images with seasonal variation of ground objects,the proposed algorithm can effectively recover the ground object information contaminated by thin clouds.关键词
遥感图像/信息恢复/图像分类/迁移学习Key words
remote sensing image/information recovery/image classification/transfer learning分类
信息技术与安全科学引用本文复制引用
胡根生,查慧敏,梁栋,鲍文霞..结合分类与迁移学习的薄云覆盖遥感图像地物信息恢复[J].电子学报,2017,45(12):2855-2862,8.基金项目
国家自然科学基金(No.61672032,No.61401001) （No.61672032,No.61401001）
安徽省自然科学基金(No.1408085MF121) （No.1408085MF121）
偏振光成像探测技术安徽省重点实验室开放课题(No.2016-KFKT-003) （No.2016-KFKT-003）
