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Enhanced visualization for the special illumination in lunar polar region 增强了月球极区特殊照明的可视化
Pub Date : 2023-01-01 DOI: 10.11834/jrs.20233334
Shen YING, Jiehao BI, Chi Chen
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引用次数: 0
Method for evaluating habitat suitability of rice sheath blight at a regional scale based on multi-source remote sensing information 基于多源遥感信息的区域尺度水稻纹枯病生境适宜性评价方法
Pub Date : 2023-01-01 DOI: 10.11834/jrs.20233219
Yangyang Tian, Kaihua Wu, Huizi Li, Yanyan Shen, Hanxiao Qiu, Jing Zhai, Jingcheng Zhang
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引用次数: 0
High-resolution remote sensing image segmentation based on dual-modal efficient feature learning 基于双峰高效特征学习的高分辨率遥感图像分割
Pub Date : 2023-01-01 DOI: 10.11834/jrs.20233162
Yinsheng ZHANG, Ru JI, Junyi TONG, Yulong YANG, Yuxiang HU, Huilin SHAN
é¥æ„Ÿå›¾åƒå› å ·æœ‰ä¸°å¯Œçš„è¯­ä¹‰ä¿¡æ¯å’Œç©ºé—´ä¿¡æ¯ï¼Œå¢žåŠ äº†è¯­ä¹‰åˆ†å‰²çš„éš¾åº¦ã€‚ç„¶è€Œå·²æœ‰æå–åŒæ¨¡æ€ç‰¹å¾çš„åˆ†å‰²æ–¹æ³•é‡‡ç”¨ç›¸åŒçš„ä¸»å¹²ç½‘ç»œï¼Œæ²¡æœ‰è€ƒè™‘äº’è¡¥ç‰¹å¾çš„å·®å¼‚ï¼Œå­˜åœ¨ç‰¹å¾æå–ã€ç‰¹å¾èžåˆå’Œä¸Šé‡‡æ ·æ¢å¤ç»†èŠ‚ä¿¡æ¯ä¸è¶³ç­‰é—®é¢˜ï¼Œæ— æ³•å‡†ç¡®é«˜æ•ˆçš„å­¦ä¹ é«˜åˆ†è¾¨çŽ‡é¥æ„Ÿå›¾åƒä¿¡æ¯ã€‚å› æ­¤ï¼Œæœ¬æ–‡æå‡ºåŸºäºŽåŒæ¨¡æ€é«˜æ•ˆç‰¹å¾å­¦ä¹ çš„é«˜åˆ†è¾¨çŽ‡é¥æ„Ÿå›¾åƒåˆ†å‰²ç®—æ³•ã€‚é¦–å ˆï¼Œé’ˆå¯¹ä¸åŒæ¨¡æ€çš„é¥æ„Ÿå›¾åƒè®¾è®¡åˆé€‚çš„ç¼–ç å™¨ï¼Œé«˜æ•ˆçš„æå–åŒæ¨¡æ€ç‰¹å¾ï¼Œå¹¶é€šè¿‡äº¤äº’åŠ å¼ºæ¨¡å—å‡å°‘ä¸åŒè·¯å¾„ç‰¹å¾ä¹‹é—´çš„å·®å¼‚ã€‚å ¶æ¬¡ï¼Œæå‡ºåŒæ¨¡æ€ç‰¹å¾èšåˆæ¨¡å—å’Œæ·±å±‚ç‰¹å¾æå–æ¨¡å—è¿›ä¸€æ­¥èžåˆå’Œæå–åŒæ¨¡æ€ç‰¹å¾ï¼Œä½¿ç½‘ç»œèƒ½å¤Ÿå åˆ†å­¦ä¹ äº’è¡¥ä¿¡æ¯ã€‚æœ€åŽï¼Œæå‡ºå¤šå±‚ç‰¹å¾ä¸Šé‡‡æ ·æ¨¡å—ï¼Œåˆ©ç”¨è¯­ä¹‰ä¿¡æ¯ä¸°å¯Œçš„é«˜å±‚ç‰¹å¾å¯¹ç»†èŠ‚ä¿¡æ¯ä¸°å¯Œçš„ä½Žå±‚ç‰¹å¾è¿›è¡ŒåŠ æƒæ“ä½œï¼Œé€æ­¥ä¸Šé‡‡æ ·å®žçŽ°ç‰¹å¾é«˜æ•ˆæ¢å¤ï¼Œæå‡åˆ†å‰²æ€§èƒ½ã€‚å®žéªŒç»“æžœè¡¨æ˜Žï¼Œæ‰€æç®—æ³•åœ¨ISPRS Potsdam和Vaihingen数据集上的总体精度分别达到了94.52%、90.45%,能够高效的提取并融合高分辨率遥感图像的双模态特征,提高遥感图像分割的准确率。
é¥æå¾åååå å -æä¸ä¸°å¯ççè¯ä¹ä¿¡æææ空é´ä¿¡æ¯æ_¡æ¯å¢å äºè¯ä¹å²çé¾åº¦ãç¶èå-²æææå模æç¹å¾ç¹å¾çåå²æ¹éç¨ç¸çåçä¸"å¹²ç½ç"ï¼æ²¡æèèäºè¡¥ç¹å¾çå-®å¼ï¼åå¨ç¹å¾æåãç¹å¾ç©ºé´ä¿¡æ¯æç¸çä¸"ï¼æ²¡æèäºè¡¥ç¹å¾çå-®å¼ï¼å¨¨ç¹å¾ç¹æãç¹å¾çåãç¹å¾ç¹èååãç¸èååååãéæ-炖汤:炖汤的时候,加入适量的盐和胡椒粉。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汤:炖汤的时候,可以加入适量的葱花、姜末、蒜末、花椒粉等调味品,炖煮的时候,可以加入适量的姜末、蒜末、花椒粉等调味品,炖煮的时候,可以加入适量的葱花、姜末、花椒粉等调味品,炖煮的时候,可以加入适量的蒜末、花椒粉等调味品,炖煮的时候,可以加入适量的葱花、姜末、花椒粉等调味品,炖煮的时候,可以加入适量的蒜末、花椒粉等调味品,炖煮的时候,可以加入适量的姜末、花椒粉等调味品。-在波茨坦瓦伊兴根的ISPRS研究中心,有94.52%,90.45%。
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引用次数: 0
Integrating ensemble prediction constraints and error prediction entropy maximization for MLS point cloud classification method 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
Pub Date : 2023-01-01 DOI: 10.11834/jrs.20233174
Xiangda Lei, Haiyan Guan, Zhen Dong
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。
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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引用次数: 0
Estimating Canopy Bulk Density and Canopy Base Height using UAV LiDAR and Multispectral Images 利用无人机激光雷达和多光谱图像估算冠层容重和冠层底高
Pub Date : 2023-01-01 DOI: 10.11834/jrs.20233094
Hao SUN, Xiaoyi GUO, Hongyan ZHANG, Jianjun ZHAO
æ£®æž—å† å±‚ä½“å¯†åº¦ï¼ˆCanopy Bulk Density,CBDï¼‰å’Œå† å±‚åŸºé«˜ï¼ˆCanopy Base Height,CBHï¼‰æ˜¯è®¸å¤šç«è¡Œä¸ºæ¨¡åž‹çš„å ³é”®è¾“å ¥å‚æ•°ã€‚ç„¶è€Œï¼Œåœ¨ä¸­å›½å¾ˆå°‘æœ‰ç ”ç©¶å ³æ³¨è¿™äº›å‚æ•°çš„ä¼°ç®—ä»¥åŠåœ¨åŒºåŸŸçš„ç©ºé—´åˆ†å¸ƒæƒ å†µã€‚æ— äººæœºæŠ€æœ¯çš„å‘å±•ä¸ºç²¾ç»†å°ºåº¦ä¼°ç®—CBD和CBHçš„ç©ºé—´åˆ†å¸ƒæä¾›äº†æœºé‡ã€‚æœ¬ç ”ç©¶é¦–å ˆåˆ©ç”¨é‡Žå¤–è°ƒæŸ¥æ•°æ®è®¡ç®—æ ·åœ°çš„CBD和CBHï¼›è€ŒåŽï¼Œåˆ©ç”¨æ— äººæœºLiDARç‚¹äº‘å’Œå¤šå ‰è°±å½±åƒï¼Œæž„å»ºåŸºäºŽé¢çŠ¶åŒºåŸŸçš„æœ€ä¼˜å­é›†å’Œéšæœºæ£®æž—ä¼°ç®—æ¨¡åž‹ï¼Œå¹¶å¯¹ä¼°ç®—ç»“æžœè¿›è¡Œè¯„ä»·ï¼›æœ€åŽï¼Œç»˜åˆ¶ç ”ç©¶åŒºçš„CBD和CBHç©ºé—´åˆ†å¸ƒå›¾ã€‚ç ”ç©¶ç»“æžœè¡¨æ˜Žï¼šé‡‡ç”¨ç›¸åŒæ•°æ®æºå’Œæ¨¡åž‹æ—¶ï¼Œä¼°ç®—CBH的R2总是高于CBD。CBD最优估算方法为融合LiDARå’Œå¤šå ‰è°±æ•°æ®çš„éšæœºæ£®æž—æ¨¡åž‹ï¼ŒR2为0.5142,RMSE为0.0773 kg/m3,rRMSE为40.73%。CBHçš„æœ€ä¼˜ä¼°ç®—æ–¹æ³•ä¸ºä» ä½¿ç”¨LiDAR数据的随机森林模型,R2为0.6477,RMSE为1.6245 m,rRMSE为31.17%。使用单一数据源时,LiDARä¼°ç®—ç²¾åº¦æ˜Žæ˜¾é«˜äºŽå¤šå ‰è°±æ•°æ®ã€‚èžåˆä¸¤ç§æ•°æ®æºä¸ä¸€å®šæå‡CBD和CBHçš„ä¼°ç®—ç²¾åº¦ã€‚æœ¬ç ”ç©¶ä¸­æž„å»ºçš„æœ€ä¼˜å­é›†æ¨¡åž‹éœ€è¦3-6ä¸ªç‰¹å¾å˜é‡ï¼Œéšæœºæ£®æž—æ¨¡åž‹åˆ™éœ€è¦è¾“å ¥10-52ä¸ªç‰¹å¾å˜é‡ã€‚ä» ä½¿ç”¨å¤šå ‰è°±å½±åƒä¼°ç®—CBD和CBHæ—¶ï¼Œæœ€ä¼˜å­é›†å›žå½’ä¼°ç®—ç²¾åº¦æ›´å¥½ï¼Œä½†æ˜¯ç©ºé—´é¢„æµ‹ç»“æžœæ˜“å—åœ°è¡¨è¦†ç›–ç±»åž‹çš„å½±å“ã€‚æœ¬ç ”ç©¶èƒ½å¤Ÿä¸ºæ£®æž—å† å±‚å¯ç‡ƒç‰©å‚æ•°ä¼°ç®—æä¾›æ–¹æ³•å‚è€ƒï¼ŒåŒæ—¶ä¹Ÿå¯ä»¥ä¸ºæž—ç«è¡Œä¸ºé¢„æµ‹æ¨¡åž‹æä¾›ç²¾ç»†å°ºåº¦çš„è¾“å ¥æ•°æ®ã€‚
树冠体积密度(CBD):树冠底座重量(CBH)。我是 "CBDåCBH "的创始人之一。LiDAR的""""""""""""""等字样,是我的最爱。CBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBDCBD5142;RMSE:0.0773 kg/m3;RMSE:40.73%。17%,LiDar的评分为3分。6个月大,10个月大52. "我是一个很有才华的人,我想成为一个很有才华的人。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。
{"title":"Estimating Canopy Bulk Density and Canopy Base Height using UAV LiDAR and Multispectral Images","authors":"Hao SUN, Xiaoyi GUO, Hongyan ZHANG, Jianjun ZHAO","doi":"10.11834/jrs.20233094","DOIUrl":"https://doi.org/10.11834/jrs.20233094","url":null,"abstract":"æ£®æž—å† å±‚ä½“å¯†åº¦ï¼ˆCanopy Bulk Density,CBDï¼‰å’Œå† å±‚åŸºé«˜ï¼ˆCanopy Base Height,CBHï¼‰æ˜¯è®¸å¤šç«è¡Œä¸ºæ¨¡åž‹çš„å ³é”®è¾“å ¥å‚æ•°ã€‚ç„¶è€Œï¼Œåœ¨ä¸­å›½å¾ˆå°‘æœ‰ç ”ç©¶å ³æ³¨è¿™äº›å‚æ•°çš„ä¼°ç®—ä»¥åŠåœ¨åŒºåŸŸçš„ç©ºé—´åˆ†å¸ƒæƒ å†µã€‚æ— äººæœºæŠ€æœ¯çš„å‘å±•ä¸ºç²¾ç»†å°ºåº¦ä¼°ç®—CBD和CBHçš„ç©ºé—´åˆ†å¸ƒæä¾›äº†æœºé‡ã€‚æœ¬ç ”ç©¶é¦–å ˆåˆ©ç”¨é‡Žå¤–è°ƒæŸ¥æ•°æ®è®¡ç®—æ ·åœ°çš„CBD和CBHï¼›è€ŒåŽï¼Œåˆ©ç”¨æ— äººæœºLiDARç‚¹äº‘å’Œå¤šå ‰è°±å½±åƒï¼Œæž„å»ºåŸºäºŽé¢çŠ¶åŒºåŸŸçš„æœ€ä¼˜å­é›†å’Œéšæœºæ£®æž—ä¼°ç®—æ¨¡åž‹ï¼Œå¹¶å¯¹ä¼°ç®—ç»“æžœè¿›è¡Œè¯„ä»·ï¼›æœ€åŽï¼Œç»˜åˆ¶ç ”ç©¶åŒºçš„CBD和CBHç©ºé—´åˆ†å¸ƒå›¾ã€‚ç ”ç©¶ç»“æžœè¡¨æ˜Žï¼šé‡‡ç”¨ç›¸åŒæ•°æ®æºå’Œæ¨¡åž‹æ—¶ï¼Œä¼°ç®—CBH的R<sup>2</sup>总是高于CBD。CBD最优估算方法为融合LiDARå’Œå¤šå ‰è°±æ•°æ®çš„éšæœºæ£®æž—æ¨¡åž‹ï¼ŒR<sup>2</sup>为0.5142,RMSE为0.0773 kg/m<sup>3</sup>,rRMSE为40.73%。CBHçš„æœ€ä¼˜ä¼°ç®—æ–¹æ³•ä¸ºä» ä½¿ç”¨LiDAR数据的随机森林模型,R<sup>2</sup>为0.6477,RMSE为1.6245 m,rRMSE为31.17%。使用单一数据源时,LiDARä¼°ç®—ç²¾åº¦æ˜Žæ˜¾é«˜äºŽå¤šå ‰è°±æ•°æ®ã€‚èžåˆä¸¤ç§æ•°æ®æºä¸ä¸€å®šæå‡CBD和CBHçš„ä¼°ç®—ç²¾åº¦ã€‚æœ¬ç ”ç©¶ä¸­æž„å»ºçš„æœ€ä¼˜å­é›†æ¨¡åž‹éœ€è¦3-6ä¸ªç‰¹å¾å˜é‡ï¼Œéšæœºæ£®æž—æ¨¡åž‹åˆ™éœ€è¦è¾“å ¥10-52ä¸ªç‰¹å¾å˜é‡ã€‚ä» ä½¿ç”¨å¤šå ‰è°±å½±åƒä¼°ç®—CBD和CBHæ—¶ï¼Œæœ€ä¼˜å­é›†å›žå½’ä¼°ç®—ç²¾åº¦æ›´å¥½ï¼Œä½†æ˜¯ç©ºé—´é¢„æµ‹ç»“æžœæ˜“å—åœ°è¡¨è¦†ç›–ç±»åž‹çš„å½±å“ã€‚æœ¬ç ”ç©¶èƒ½å¤Ÿä¸ºæ£®æž—å† å±‚å¯ç‡ƒç‰©å‚æ•°ä¼°ç®—æä¾›æ–¹æ³•å‚è€ƒï¼ŒåŒæ—¶ä¹Ÿå¯ä»¥ä¸ºæž—ç«è¡Œä¸ºé¢„æµ‹æ¨¡åž‹æä¾›ç²¾ç»†å°ºåº¦çš„è¾“å ¥æ•°æ®ã€‚","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135159781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mapping typical floodplain wetlands using the improved D-UNet model 利用改进的D-UNet模型对典型洪泛平原湿地进行制图
Pub Date : 2023-01-01 DOI: 10.11834/jrs.20233087
Jiudan ZHANG, Haoyu WANG, Junli LI, Anming BAO, Haoru WU, Shuangshuang Li, Zhanfeng Shen
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引用次数: 0
The Cognitive Logic and Map Construction Model of Machine Maps 机器地图的认知逻辑和地图构建模型
Pub Date : 2023-01-01 DOI: 10.11834/jrs.20233066
Fenli JIA, Jian YANG, Xiong YOU, Ke LI, Jiangpeng TIAN, Shulei ZHENG
æœºå™¨åœ°å›¾æ˜¯ä¸ºæå‡æ— äººå¹³å°å¤æ‚çŽ¯å¢ƒè®¤çŸ¥ä¸Žç†è§£èƒ½åŠ›è€Œæå‡ºçš„ä¸€ç±»æ–°åž‹åœ°å›¾ã€‚æœ¬æ–‡ä»¥æœºå™¨åœ°å›¾çš„æ¦‚å¿µæ¨¡åž‹ä¸ºåŸºç¡€ï¼Œä»Žè®¤çŸ¥ç§‘å­¦è§†è§’æå‡ºä¸€ç§ä»¥è®°å¿†ç»“æž„ä¸Žè¿‡ç¨‹ä¸ºå‚è€ƒã€ç¬¦åˆè®¤çŸ¥é€»è¾‘çš„æœºå™¨åœ°å›¾å»ºå›¾ç†è®ºæ¨¡åž‹ã€‚é€šè¿‡åˆ†æžè®°å¿†çš„ç»“æž„ä¸Žè¿‡ç¨‹ã€å¿ƒè±¡åœ°å›¾çš„å† å®¹ä¸Žç»„ç»‡ï¼Œè®¤çŸ¥æž¶æž„å’Œæœºå™¨äººç³»ç»Ÿä¸­çš„çŽ¯å¢ƒè®¤çŸ¥ç­‰é—®é¢˜ï¼Œæ¢³ç†å‡ºæœºå™¨åœ°å›¾çš„è®¤çŸ¥é€»è¾‘åŠå ¶å¯¹æœºå™¨åœ°å›¾å»ºå›¾æ¨¡åž‹çš„æ”¯æ’‘ã€‚åœ¨æ­¤åŸºç¡€ä¸Šï¼Œåˆ†æžäº†æœºå™¨åœ°å›¾çš„ä»»åŠ¡ç›®æ ‡å’Œå† å®¹åˆ†ç±»ï¼Œä»Žä¿¡æ¯ç»„ç»‡ã€é€»è¾‘ç»“æž„ä¸Žç”Ÿæˆè¿‡ç¨‹ä¸‰æ–¹é¢æå‡ºæœºå™¨åœ°å›¾å»ºå›¾æ¨¡åž‹çš„è®¾è®¡åŽŸåˆ™ã€‚åŸºäºŽè¯¥åŽŸåˆ™ï¼Œè®¾è®¡äº†æœºå™¨åœ°å›¾å»ºå›¾çš„é€»è¾‘ç»“æž„å’Œè¿‡ç¨‹æ¨¡åž‹ã€‚åœ¨é€»è¾‘ç»“æž„æ–¹é¢ï¼Œä»Žç©ºé—´ã€è§†è§‰ã€æƒ å¢ƒã€å›¾å¼ã€è§„åˆ™ç­‰æ–¹é¢ç»†åŒ–äº†æ„ŸçŸ¥åœ°å›¾ã€å·¥ä½œåœ°å›¾å’Œé•¿æ—¶åœ°å›¾ä¸­çš„å† å®¹ä¸Žç»“æž„å ³ç³»ï¼›åœ¨è¿‡ç¨‹æ¨¡åž‹æ–¹é¢ï¼ŒæŒ‰ç §è®¤çŸ¥æ´»åŠ¨æŠ½è±¡å‡ºåŒ æ‹¬ç†è§£ã€æ³¨æ„ã€æŽ¨ç†ã€å­¦ä¹ ã€è¡ŒåŠ¨çš„æœºå™¨åœ°å›¾å»ºå›¾åŸºæœ¬æ´»åŠ¨ï¼Œå¹¶æå‡ºå† éšå’Œå¤–æ˜¾ä¸¤ç±»å»ºå›¾è¿‡ç¨‹ã€‚æœºå™¨åœ°å›¾çš„è®¤çŸ¥é€»è¾‘ä¸Žå»ºå›¾æ¨¡åž‹ï¼Œæœ¬è´¨ä¸Šæ˜¯å¯¹æœºå™¨åœ°å›¾è®¤çŸ¥è®¡ç®—æœºåˆ¶çš„é˜é‡Šï¼Œä»ŽæŠ½è±¡å±‚æ¬¡ä¸Šä¸ºç ”ç©¶äººå‘˜æä¾›ååŒç ”ç©¶çš„åŸºç¡€æ¡†æž¶ï¼Œä¹Ÿä¸ºç›¸å ³æŠ€æœ¯ã€æ•°æ®çš„é›†æˆã€è¯„ä¼°ã€è¿ç”¨æä¾›å‚è€ƒã€‚æœ¬æ–‡çš„ç ”ç©¶è¿˜ä¸ºæ•°å­—å­ªç”Ÿæˆ–è™šæ‹Ÿåœ°ç†çŽ¯å¢ƒçš„æž„å»ºæå‡ºæ–°çš„ç›®æ ‡ä¸Žè¦æ±‚ã€‚
我是一名学生。人平å¤æç¯åæç¢è®¤ç¥ä¸ç解è½åèæåºçä¸ç±"æ°ååå¾ãæ¬æä"¥æºå¨å°å°å°å¾çæ¦å¿µæ¨¡å为åºç¡ï¼ä"认ç¥ç§§è妧è§è§è§æåºä¸ç§ä"¥è®°å¿ç"æä¸è¿ç¨ä¸ºåèã符å认ç¥é"è¾çæºåºåå¨å°½åèæåºçä¸ç±"æ°åå¾ãæ¬ç"æä¸è¿ç¨ãå¿è±¡¡å¾å¾ç我是个很有耐心的人。炖牛肉的做法炖汤: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,汤的味道越淡。餈銵冽靘憒餃僑頧阡瘣餃冽靘憒餃僑頧阡瘣餃冽靘憒餃僑頧阡瘣餃冽靘憒餃僑頧阡瘣餃冽靘憒餃僑頧阡瘣餃冽靘憒餃僑頧阡瘣餃冽靘憒餃僑頧阡瘣餃冽靘憒餃僑頧阡瘣餃冽靘憒餃僑頧阡瘣餃冽靘憒
{"title":"The Cognitive Logic and Map Construction Model of Machine Maps","authors":"Fenli JIA, Jian YANG, Xiong YOU, Ke LI, Jiangpeng TIAN, Shulei ZHENG","doi":"10.11834/jrs.20233066","DOIUrl":"https://doi.org/10.11834/jrs.20233066","url":null,"abstract":"æœºå™¨åœ°å›¾æ˜¯ä¸ºæå‡æ— äººå¹³å°å¤æ‚çŽ¯å¢ƒè®¤çŸ¥ä¸Žç†è§£èƒ½åŠ›è€Œæå‡ºçš„ä¸€ç±»æ–°åž‹åœ°å›¾ã€‚æœ¬æ–‡ä»¥æœºå™¨åœ°å›¾çš„æ¦‚å¿µæ¨¡åž‹ä¸ºåŸºç¡€ï¼Œä»Žè®¤çŸ¥ç§‘å­¦è§†è§’æå‡ºä¸€ç§ä»¥è®°å¿†ç»“æž„ä¸Žè¿‡ç¨‹ä¸ºå‚è€ƒã€ç¬¦åˆè®¤çŸ¥é€»è¾‘çš„æœºå™¨åœ°å›¾å»ºå›¾ç†è®ºæ¨¡åž‹ã€‚é€šè¿‡åˆ†æžè®°å¿†çš„ç»“æž„ä¸Žè¿‡ç¨‹ã€å¿ƒè±¡åœ°å›¾çš„å† å®¹ä¸Žç»„ç»‡ï¼Œè®¤çŸ¥æž¶æž„å’Œæœºå™¨äººç³»ç»Ÿä¸­çš„çŽ¯å¢ƒè®¤çŸ¥ç­‰é—®é¢˜ï¼Œæ¢³ç†å‡ºæœºå™¨åœ°å›¾çš„è®¤çŸ¥é€»è¾‘åŠå ¶å¯¹æœºå™¨åœ°å›¾å»ºå›¾æ¨¡åž‹çš„æ”¯æ’‘ã€‚åœ¨æ­¤åŸºç¡€ä¸Šï¼Œåˆ†æžäº†æœºå™¨åœ°å›¾çš„ä»»åŠ¡ç›®æ ‡å’Œå† å®¹åˆ†ç±»ï¼Œä»Žä¿¡æ¯ç»„ç»‡ã€é€»è¾‘ç»“æž„ä¸Žç”Ÿæˆè¿‡ç¨‹ä¸‰æ–¹é¢æå‡ºæœºå™¨åœ°å›¾å»ºå›¾æ¨¡åž‹çš„è®¾è®¡åŽŸåˆ™ã€‚åŸºäºŽè¯¥åŽŸåˆ™ï¼Œè®¾è®¡äº†æœºå™¨åœ°å›¾å»ºå›¾çš„é€»è¾‘ç»“æž„å’Œè¿‡ç¨‹æ¨¡åž‹ã€‚åœ¨é€»è¾‘ç»“æž„æ–¹é¢ï¼Œä»Žç©ºé—´ã€è§†è§‰ã€æƒ å¢ƒã€å›¾å¼ã€è§„åˆ™ç­‰æ–¹é¢ç»†åŒ–äº†æ„ŸçŸ¥åœ°å›¾ã€å·¥ä½œåœ°å›¾å’Œé•¿æ—¶åœ°å›¾ä¸­çš„å† å®¹ä¸Žç»“æž„å ³ç³»ï¼›åœ¨è¿‡ç¨‹æ¨¡åž‹æ–¹é¢ï¼ŒæŒ‰ç §è®¤çŸ¥æ´»åŠ¨æŠ½è±¡å‡ºåŒ æ‹¬ç†è§£ã€æ³¨æ„ã€æŽ¨ç†ã€å­¦ä¹ ã€è¡ŒåŠ¨çš„æœºå™¨åœ°å›¾å»ºå›¾åŸºæœ¬æ´»åŠ¨ï¼Œå¹¶æå‡ºå† éšå’Œå¤–æ˜¾ä¸¤ç±»å»ºå›¾è¿‡ç¨‹ã€‚æœºå™¨åœ°å›¾çš„è®¤çŸ¥é€»è¾‘ä¸Žå»ºå›¾æ¨¡åž‹ï¼Œæœ¬è´¨ä¸Šæ˜¯å¯¹æœºå™¨åœ°å›¾è®¤çŸ¥è®¡ç®—æœºåˆ¶çš„é˜é‡Šï¼Œä»ŽæŠ½è±¡å±‚æ¬¡ä¸Šä¸ºç ”ç©¶äººå‘˜æä¾›ååŒç ”ç©¶çš„åŸºç¡€æ¡†æž¶ï¼Œä¹Ÿä¸ºç›¸å ³æŠ€æœ¯ã€æ•°æ®çš„é›†æˆã€è¯„ä¼°ã€è¿ç”¨æä¾›å‚è€ƒã€‚æœ¬æ–‡çš„ç ”ç©¶è¿˜ä¸ºæ•°å­—å­ªç”Ÿæˆ–è™šæ‹Ÿåœ°ç†çŽ¯å¢ƒçš„æž„å»ºæå‡ºæ–°çš„ç›®æ ‡ä¸Žè¦æ±‚ã€‚","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135549910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Hybrid Spatial Pyramid Pooling Network to Change Detection for High Resolution Remote Sensing Images 基于混合空间金字塔池网络的高分辨率遥感图像变化检测
Pub Date : 2023-01-01 DOI: 10.11834/jrs.20233229
Pan SHAO, Ziang GAO
ä»¥æ®‹å·®ç½‘ç»œä¸ºåŸºç¡€ï¼Œç»“åˆé«˜åˆ†è¾¨çŽ‡é¥æ„Ÿå½±åƒçš„ç‰¹ç‚¹ï¼Œæå‡ºä¸€ç§å ¨æ–°çš„ç«¯åˆ°ç«¯å˜åŒ–æ£€æµ‹ç½‘ç»œ--æ··åˆç©ºé—´é‡‘å­—å¡”æ± åŒ–ç½‘ç»œ (Hybrid spatial pyramid pooling network,HSPPNet)。HSPPNeté¦–å ˆå°†ç©ºæ´žå·ç§¯å’Œæ³¨æ„åŠ›æœºåˆ¶å¼•å¯¼å·ç§¯å¹¶è¡Œé›†æˆï¼Œæž„å»ºä¸€ç§æ··åˆç©ºé—´é‡‘å­—å¡”æ± åŒ–æ¨¡å—ï¼Œä»¥ä¾¿æœ‰æ•ˆæå–é«˜åˆ†è¾¨çŽ‡é¥æ„Ÿå½±åƒä¸­ä¸åŒå½¢çŠ¶å°ºåº¦çš„å˜åŒ–å¯¹è±¡ã€‚ç„¶åŽï¼Œé€šè¿‡å®šä¹‰ä¸€ç§æˆªæ–­-è¡¥å¿åŠ æƒäº¤å‰ç†µå‡½æ•°å’Œä¸€ç§ç±»åˆ«çº§IoUå‡½æ•°å¹¶å°†äºŒè€ é›†æˆï¼Œå¾—åˆ°ä¸€ç§å ¨æ–°çš„è‡ªé€‚åº”å¹³è¡¡æŸå¤±å‡½æ•°ï¼Œæ¥é™ä½Žå˜åŒ–ç±»ä¸Žæœªå˜åŒ–ç±»ä¸¥é‡ä¸å‡è¡¡é—®é¢˜å¯¹å˜åŒ–æ£€æµ‹çš„å½±å“ã€‚æœ€åŽè®¾è®¡ä¸€ç§ç®€å•æœ‰æ•ˆçš„è¾“å ¥æ¨¡å—ï¼Œé€šè¿‡ç»¼åˆè€ƒè™‘ä¸¤æœŸé¥æ„Ÿå½±åƒåŠå ¶å·®å¼‚å›¾æ¥å¢žå¼ºå˜åŒ–ä¿¡æ¯ã€‚é€šè¿‡ä»¥ä¸Šä¸‰ç‚¹ï¼ŒHSPPNetå¢žå¼ºäº†æ·±åº¦å­¦ä¹ å˜åŒ–æ£€æµ‹çš„æ€§èƒ½ã€‚ä¸¤ç»„å¸¸ç”¨å ¬å¼€å˜åŒ–æ£€æµ‹æ•°æ®é›†ä¸Šçš„å®žéªŒç»“æžœè¡¨æ˜ŽHSPPNet可行、有效。
混合空间金字塔集合网络(HSPPN混合空间金字塔集合网络(HSPPNet)。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牛肉、炖羊肉、炖鸡、炖鱼、炖牛肉、炖猪肉、炖牛肉、炖鸡HSPPNet的HSPPNET的HSPPNET的HSPPNET的
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引用次数: 0
Dynamic Changes of Huizhou Coastline in nearly 50 Years Based on Landsat Images and DSAS 基于Landsat和DSAS的惠州海岸线近50年动态变化
Pub Date : 2023-01-01 DOI: 10.11834/jrs.20231475
Tonghao WANG, Guifang ZHANG, Ke ZHANG, Qiang FU
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引用次数: 0
Extraction method of mangrove single tree growth parameters based on combinational Airborne-Ground LiDAR 基于机载-地面组合激光雷达的红树林单树生长参数提取方法
Pub Date : 2023-01-01 DOI: 10.11834/jrs.20233148
Jingrui ZHANG, Guangbo REN, Peiqiang WU, Chengfei ZHANG, Shanwei LIU, Yi MA
{"title":"Extraction method of mangrove single tree growth parameters based on combinational Airborne-Ground LiDAR","authors":"Jingrui ZHANG, Guangbo REN, Peiqiang WU, Chengfei ZHANG, Shanwei LIU, Yi MA","doi":"10.11834/jrs.20233148","DOIUrl":"https://doi.org/10.11834/jrs.20233148","url":null,"abstract":"","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135505392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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