太阳成8722(中国)有限公司-GREEN NO.1

标题: Block Adjustment-Based Radiometric Normalization by Considering Global and Local Differences

作者: Zhang, XS (Zhang, Xiaoshuang); Feng, RT (Feng, Ruitao); Li, XH (Li, Xinghua); Shen, HF (Shen, Huanfeng); Yuan, ZX (Yuan, Zhaoxiang)

来源出版物: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 卷: 19 文献号: 8002805 DOI: 10.1109/LGRS.2020.3031398 出版年: 2022

摘要: For radiometric normalization (RN) of multiple remote sensing (MRS) images within large-scale coverage, the traditional methods ignore the error accumulation and adaptive allocation of cumulative errors caused by the transfer paths in the classical one-after-another pipeline. To this end, a block adjustment-based RN method of MRS images is proposed by considering the global and local radiometric differences (RDs) in this letter. First, the block adjustment-based global RN is conducted to eliminate the global differences of MRS images. This step is independent of transfer paths so that it breaks through the corresponding error accumulation and uneven distribution in the one-after-another pipeline. Second, two local strategies based on block adjustment and edge optimization are further adopted to remove the local residual RDs. In the experiments, it demonstrates that the proposed method can obtain MRS images with a balanced and appealing visual effect, which outperforms the moment matching (MM) method and the popular ENVI software.

作者关键词: Mathematical model; Radiometry; Remote sensing; Image color analysis; Standards; Biological system modeling; Adaptation models; Block adjustment; local difference; moment matching (MM); radiometric normalization (RN); remote sensing image

地址: [Zhang, Xiaoshuang; Feng, Ruitao; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Li, Xinghua] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.

[Yuan, Zhaoxiang] State Power Econ & Technol Res Inst Co Ltd, Beijing 100120, Peoples R China.

通讯作者地址: Li, XH (通讯作者)，Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.

电子邮件地址: zhangxiaoshuang@whu.edu.cn; ruitaofeng@whu.edu.cn; lixinghua5540@whu.edu.cn; shenhf@whu.edu.cn; yuanzhaoxiang@chinasperi.sgcc.com.cn

影响因子：3.966