ZY-3多角度遥感图像中的云检测

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL
Haiyan Huang, Q. Cheng, Yin Pan, N. Lyimo, Hao Peng, Gui Cheng
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引用次数: 0

摘要

云污染对遥感图像的影响严重影响了遥感图像的实际利用率。因此,遥感图像的云检测是图像预处理和图像可用性筛选中不可缺少的一部分。针对ZY-3高分辨率卫星图像缺少短波红外和热红外波段导致检测效果不佳的问题,考虑到云和地面物体地理高度存在明显差异,本文提出了一种基于多尺度特征-卷积神经网络(MF-CNN)模型的光谱信息与数字高度模型(DHM)相结合的厚薄云检测方法。为了验证DHM高度信息在ZY-3多角度遥感图像云检测中的重要性,本文基于MF-CNN模型,对有DHM高度信息和没有DHM高度信息的数据集进行云检测对比。实验结果表明,具有DHM高度信息的ZY-3多角度图像可以有效地改善高光地表与薄云的混淆,这也意味着DHM高度信息的辅助可以弥补高分辨率图像缺乏短波红外和热红外波段的缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud Detection in ZY-3 Multi-Angle Remote Sensing Images
Cloud pollution on remote sensing images seriously affects the actual use rate of remote sensing images. Therefore, cloud detection of remote sensing images is an indispensable part of image preprocessing and image availability screening. Aiming at the lack of short wave infrared and thermal infrared bands in ZY-3 high-resolution satellite images resulting in the poor detection effect, considering the obvious difference in geographic height between cloud and ground surface objects, this paper proposes a thick and thin cloud detection method combining spectral information and digital height model (DHM) based on multi-scale features-convolutional neural network (MF-CNN) model. To verify the importance of DHM height information in cloud detection of ZY-3 multi-angle remote sensing images, this paper implements cloud detection comparison of the data set with and without DHM height information based on the MF-CNN model. The experimental results show that the ZY-3 multi-angle image with DHM height information can effectively improve the confusion between highlighted surface and thin cloud, which also means the assistance of DHM height information can make up for the disadvantage of high-resolution image lacking short wave infrared and thermal infrared bands.
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来源期刊
Photogrammetric Engineering and Remote Sensing
Photogrammetric Engineering and Remote Sensing 地学-成像科学与照相技术
CiteScore
1.70
自引率
15.40%
发文量
89
审稿时长
9 months
期刊介绍: Photogrammetric Engineering & Remote Sensing commonly referred to as PE&RS, is the official journal of imaging and geospatial information science and technology. Included in the journal on a regular basis are highlight articles such as the popular columns “Grids & Datums” and “Mapping Matters” and peer reviewed technical papers. We publish thousands of documents, reports, codes, and informational articles in and about the industries relating to Geospatial Sciences, Remote Sensing, Photogrammetry and other imaging sciences.
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