摘要
针对海岸地形航空摄影遥感影像分辨率低导致测量测量低问题,研究超分辨率学习算法在海岸地形航空摄影测量中的应用。基于无人机海岸地形航测遥感影像数据,利用超分辨率学习算法对影像数据进行重建获得高分辨率海岸地形遥感影像;通过最大类间方差法对遥感影像进行分类,并采用反距离加权值法进行匹配,获得海岸地形地理信息。实验结果表明:相比传统方法,本文方法可以获取较高的分辨率遥感影像以及海岸地形航空摄影测量精度,遥感影像峰值信噪比(PSNR)指标平均值在19 dB以上,平面误差与高程误差均小于等于±1.50。
Aiming at the problem of low resolution of coastal terrain aerial photogrammetric remote sensing images,the application of super-resolution learning algorithms in coastal terrain aerial photogrammetry was studied.Based on the aerial remote sensing image data of unmanned aerial vehicles(UAV)coastal terrain,a high-resolution coastal terrain remote sensing image is obtained by reconstructing the image data using a super-resolution learning algorithm.Remote sensing images are classified using the maximum inter class variance method and matched using the inverse distance weighted value method to obtain coastal topographic and geographic information.Experimental results show that compared to traditional methods,this method can obtain higher resolution remote sensing images and coastal terrain aerial photogrammetry accuracy.The average PSNR index of remote sensing images is above 19 dB,and the plane error and elevation error are both less than or equal to±1.50.
作者
马金龙
MA Jinlong(China Coal Aerial Survey Remote Sensing Group Company Limited,Xi'an,Shaanxi 710199 China)
出处
《北京测绘》
2023年第8期1141-1147,共7页
Beijing Surveying and Mapping
关键词
遥感影像
海岸地形测量
航空摄影
数字高程模型
最小二乘法
遥感影像校正
remote sensing image
coastal topographic survey
aerial photography
digital elevation model
least square method
remote sensing image correction
