基于港口船舶识别的高分二号卫星遥感影像融合方法比较

doi:10.3969j.issn.1001-909X.2022.04.009

海洋学研究 ›› 2022, Vol. 40 ›› Issue (4): 90-96.DOI: 10.3969j.issn.1001-909X.2022.04.009

• 研究报道 • 上一篇

基于港口船舶识别的高分二号卫星遥感影像融合方法比较

赵益智^1,2, 朱海天³, 李修楠^1,2, 杨劲松^1,2, 陈鹏^*1,2

1.自然资源部第二海洋研究所,浙江杭州 310012;
2.卫星海洋环境动力学国家重点实验室,浙江杭州 310012;
3.国家卫星海洋应用中心,北京 100081

收稿日期:2022-03-01 出版日期:2022-12-15 发布日期:2023-02-09
通讯作者: ^*陈鹏(1977—),男,正高级工程师,主要从事卫星海洋目标遥感探测与仿真研究,E-mail:chenpeng@sio.org.cn。
作者简介:赵益智(1997—),女,浙江省温州市人,主要从事海洋遥感研究,E-mail:zhaoyizhi1119@163.com。
基金资助:
高分海洋资源环境遥感信息处理与业务应用示范系统(二期)(41-Y30F07-9001-20/22);海洋领域融合应用示范项目

Comparative research on image fusion methods of GF-2 satellite based on port ship recognition

ZHAO Yizhi^1,2, ZHU Haitian³, LI Xiunan^1,2, YANG Jingsong^1,2, CHEN Peng^*1,2

1. Second Institute of Oceanography, MNR, Hangzhou 310012, China;
2. State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou 310012, China;
3. National Satellite Ocean Application Service, Beijing 100081, China

Received:2022-03-01 Online:2022-12-15 Published:2023-02-09

摘要/Abstract

摘要： 在卫星遥感影像识别中,相较于海上单一环境的船舶识别,港口船舶识别由于存在集装箱、码头等大量干扰目标,显得更为困难。采用强度-色度-饱和度(Intensity-Hue-Saturation,IHS)变换、Brovey变换(Brovey Transform,BT)、ESRI全色锐化变换、简单均值变换和施密特正交变换法(Gram-Schmidt,GS)等5种融合算法,进行高分二号卫星全色和多光谱影像的融合试验,通过定性和定量评价选出适用于港口船舶影像的最优方法。结果显示GS融合方法在增加影像空间信息的同时维持了光谱保真性,其均方根误差、峰值信噪比、结构相似性等指标均优于其他4种融合方法,可用于港口船舶识别。

关键词: 遥感, 影像融合, 港口船舶, 高分二号卫星影像

Abstract: In satellite remote sensing image recognition, compared with ship recognition in a single marine environment, port ship recognition is more difficult due to the presence of a large number of interference targets such as containers and docks. In order to improve the recognition ability of GF-2 satellite data on port ships, five fusion algorithms, i.e. Intensity Hue Saturation (IHS) Transform, Brovey Transform(BT), ESRI panchromatic sharpening Transform, simple mean Transform and Gram-Schmidt Transform (GS) were used to perform the fusion experiment of panchromatic and multispectral images, and the optimal method applicable to port ship images is selected through qualitative and quantitative evaluation. The results show that GS Transform can increase spatial information while maintaining spectral fidelity, and its mean value, root mean square error, peak signal to noise ratio, and structural similarity are superior to the other four fusion algorithms, with high recognition accuracy for port ships.

Key words: remote sensing, image fusion, port ship, GF-2 satellite data

中图分类号:

TP75

赵益智, 朱海天, 李修楠, 杨劲松, 陈鹏. 基于港口船舶识别的高分二号卫星遥感影像融合方法比较[J]. 海洋学研究, 2022, 40(4): 90-96.

ZHAO Yizhi, ZHU Haitian, LI Xiunan, YANG Jingsong, CHEN Peng. Comparative research on image fusion methods of GF-2 satellite based on port ship recognition[J]. Journal of Marine Sciences, 2022, 40(4): 90-96.

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基于港口船舶识别的高分二号卫星遥感影像融合方法比较

Comparative research on image fusion methods of GF-2 satellite based on port ship recognition

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