Point-based multi-scale morphological reconstruction filter

نویسندگان

چکیده

é’ˆå¯¹çŽ°æœ‰æœºè½½æ¿€å ‰é›·è¾¾ï¼ˆLiDAR)点云滤波算法难以准确分离复杂地形中地面点与地物点问题,提出了一种基于点的多尺度形态学重建滤波方法PMMF(Point-based Multi-scale Morphological reconstruction Filter)。在初始尺度层次下,PMMFé€šè¿‡æž„å»ºä¸€ç§åŸºäºŽç‚¹çš„å½¢æ€å­¦é‡å»ºå¯¹åŽŸå§‹ç‚¹äº‘æ»¤æ³¢ï¼Œå³å ˆåœ¨æŽ©è†œç‚¹äº‘çº¦æŸä¸‹å€ŸåŠ©ké‚»åŸŸç»“æž„å ƒç´ å’Œé«˜ç¨‹ç¼“å†²åŒºåå¤è†¨èƒ€æ ‡è®°ç‚¹äº‘ï¼ŒèŽ·å–æ½œåœ¨åœ°é¢ç‚¹ï¼›ç„¶åŽé€šè¿‡è‡ªé€‚åº”å¡åº¦æ–¹æ³•å‰”é™¤æ½œåœ¨åœ°é¢ç‚¹ä¸­çš„éžåœ°é¢ç‚¹ï¼Œå ¶ä¸­ï¼Œå¡åº¦é˜ˆå€¼éšåœ°å½¢å¤æ‚åº¦è‡ªé€‚åº”å˜åŒ–ã€‚åœ¨ä¸Šå±‚æ»¤æ³¢ç»“æžœåŸºç¡€ä¸Šï¼ŒPMMFé€šè¿‡æå‡ç§å­ç‚¹é€‰æ‹©çš„ç½‘æ ¼å°ºåº¦é‡å¤ä¸Šå±‚æ»¤æ³¢è¿‡ç¨‹ï¼Œç›´è‡³ç»“æžœæ”¶æ•›ã€‚ä»¥å›½é™ æ‘„å½±æµ‹é‡ä¸Žé¥æ„Ÿå­¦ä¼šï¼ˆISPRS)发布的15ç»„åŸºå‡†æ•°æ®ä¸ºç ”ç©¶å¯¹è±¡ï¼Œå°†PMMF滤波结果与近5年(2016年—2020年)提出的15种滤波算法比较表明,PMMF有8ç»„æ•°æ®æ»¤æ³¢æ•ˆæžœå ä¼˜ï¼Œ15组数据平均总误差和Kappa系数分别为2.71%和91.08%。使用4种不同地形特征的高密度机载LiDAR点云数据进一步验证PMMF的滤波效果,并将计算结果与简单形态学滤波(SMRF)、布料模拟滤波(CSFï¼‰ã€æ¸è¿›åŠ å¯†ä¸‰è§’ç½‘æ»¤æ³¢ï¼ˆPTD)和多分辨率层次滤波(MHF)比较。结果表明,PMMF滤波性能最优,平均总误差为3.24%ï¼Œè¾ƒå ¶ä»–4种滤波方法分别减小了12.0%、59.1%、70.1%和53.2%。

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ژورنال

عنوان ژورنال: Journal of remote sensing

سال: 2022

ISSN: ['1007-4619', '2095-9494']

DOI: https://doi.org/10.11834/jrs.20210314