集成集成预测约束和误差预测熵最大化的MLS点云分类方法

遥感学报 Pub Date : 2023-01-01 DOI:10.11834/jrs.20233174
Xiangda Lei, Haiyan Guan, Zhen Dong
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引用次数: 0

摘要

餈銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵冽嫣噶瘜敺撣貉銵我很喜欢这篇文章。移动式激光扫描仪 MLS我很高兴能在这里工作。炖菜炖汤:炖汤的时候要注意炖的时间,炖的时间越长,汤的味道就越浓,炖的时间越长,汤的味道就越淡,炖的时间越长,汤的味道就越浓。炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮炖煮我想去多伦多3DWHU看看。MLS巴黎83。68%ã44.19%ã65.85%ï¼ï¼è¡¨æäºæ¹æ³æ¹ççææææ§ã
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Integrating ensemble prediction constraints and error prediction entropy maximization for MLS point cloud classification method
ç›®å‰ï¼Œè®¸å¤šæ·±åº¦å­¦ä¹ ç‚¹äº‘åˆ†ç±»æ–¹æ³•é€šè¿‡å¢žåŠ ç‚¹äº‘ç‰¹å¾èšåˆæ¨¡å—ï¼Œå¢žå¼ºç‚¹äº‘ç‰¹å¾çš„è¡¨è¾¾èƒ½åŠ›ã€‚ä½†è¯¥ç±»æ–¹æ³•å¾€å¾€ä¼šå¸¦æ¥è®­ç»ƒå‚æ•°å¢žåŠ ä»¥åŠæ¨¡åž‹è¿‡æ‹Ÿåˆçš„é—®é¢˜ã€‚é’ˆå¯¹è¯¥é—®é¢˜ï¼Œæœ¬æ–‡æå‡ºäº†ä¸€ä¸ªæ•´åˆé›†æˆé¢„æµ‹çº¦æŸä¸Žé”™è¯¯é¢„æµ‹ç†µæœ€å¤§åŒ–çš„æ·±åº¦å­¦ä¹ æ–¹æ³•ç”¨äºŽç§»åŠ¨æ¿€å ‰æ‰«æï¼ˆMobile Laser Scanning, MLSï¼‰ç‚¹äº‘åˆ†ç±»ã€‚æ–¹æ³•é€šè¿‡é›†æˆé¢„æµ‹çº¦æŸåˆ†æ”¯ä»¥åŠé”™è¯¯é¢„æµ‹ç†µæœ€å¤§åŒ–åˆ†æ”¯å¯ä»¥åœ¨ä¸å¢žåŠ è®­ç»ƒå‚æ•°çš„æƒ å†µä¸‹ï¼Œå¢žå¼ºåŸºçº¿ç½‘ç»œçš„ç‚¹äº‘ç‰¹å¾è¡¨è¾¾ï¼Œæé«˜æ¨¡åž‹æ³›åŒ–èƒ½åŠ›ã€‚å ¶ä¸­é›†æˆé¢„æµ‹çº¦æŸåˆ†æ”¯é¦–å ˆé€šè¿‡è®°å½•ç‚¹äº‘åœ¨è®­ç»ƒè¿‡ç¨‹ä¸­çš„é¢„æµ‹å€¼ï¼Œç”Ÿæˆé›†æˆé¢„æµ‹å€¼ï¼Œç„¶åŽé‡‡ç”¨ä¸€è‡´æ€§çº¦æŸå¢žå¼ºæ¨¡åž‹çš„ç‚¹äº‘ç‰¹å¾è¡¨è¾¾ã€‚é”™è¯¯é¢„æµ‹ç†µæœ€å¤§åŒ–æ–¹æ³•é¼“åŠ±æ¨¡åž‹å¯¹é”™è¯¯é¢„æµ‹ç‚¹è¿›è¡Œç†µå€¼æœ€å¤§åŒ–ï¼Œå¢žåŠ è¯¥ç‚¹çš„ä¸ç¡®å®šæ€§ï¼Œæé«˜æ¨¡åž‹çš„æ³›åŒ–èƒ½åŠ›ã€‚æ‰€ææ–¹æ³•åœ¨å¤šä¸ªå ¬å¼€MLSç‚¹äº‘æ•°æ®é›†ä¸Šè¿›è¡ŒéªŒè¯ï¼Œç»“æžœè¡¨æ˜Žæ‰€ææ–¹æ³•å¯ä»¥åœ¨ä¸å¢žåŠ è®­ç»ƒå‚æ•°çš„æƒ å†µä¸‹ï¼Œæé«˜åŸºçº¿æ–¹æ³•çš„åˆ†ç±»æ€§èƒ½ã€‚ä¸Žå¯¹æ¯”æ–¹æ³•ç›¸æ¯”ï¼Œæ‰€ææ–¹æ³•åœ¨Toronto3D、WHU-MLS、Paris数据集上获得了最优的平均交并比(83.68%、44.19%、65.85%),表明了方法的有效性。
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遥感学报
遥感学报 Social Sciences-Geography, Planning and Development
CiteScore
3.60
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3200
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