Remote sensing mineralization alteration information extraction based on PCA, Multilevel Segment Method, and SVM

نویسندگان

چکیده

ä¸ºäº†åˆ©ç”¨é¥æ„Ÿå½±åƒè¿›è¡Œæ›´åŠ ç²¾ç¡®çš„æ‰¾çŸ¿é¢„æµ‹ï¼Œæœ¬æ–‡é€‰æ‹©æ–°ç–†ä¸œå¤©å±±å°¾äºšåœ°åŒºASTERæ•°æ®è¿›è¡ŒçŸ¿åŒ–èš€å˜ä¿¡æ¯æå–æ–¹æ³•ç ”ç©¶ã€‚ä¸ºäº†æé«˜ä¿¡æ¯æå–ç²¾åº¦ï¼Œæœ¬æ–‡æå‡ºäº†ç»“åˆä¸»æˆåˆ†åˆ†æžï¼ˆPCA)、多尺度分割和支持向量机(SVMï¼‰çš„é¥æ„ŸçŸ¿åŒ–èš€å˜ä¿¡æ¯æå–æ–¹æ³•ã€‚é¦–å ˆï¼Œåˆ†æžASTERæ•°æ®çš„ç‰¹å¾ï¼Œé€‰å–å„çŸ¿åŒ–èš€å˜ä¿¡æ¯çš„ç‰¹å¾æ³¢æ®µï¼Œå¯¹ç»„åˆæ³¢æ®µè¿›è¡Œä¸»æˆåˆ†åˆ†æžï¼ŒèŽ·å¾—ä¸»åˆ†é‡å›¾åƒï¼›ç„¶åŽï¼Œå¯¹å„ä¸»åˆ†é‡å›¾åƒè¿›è¡Œå¤šå°ºåº¦åˆ†å‰²ï¼Œå¹¶èŽ·å¾—åˆ†å‰²ä¹‹åŽçš„å‡å€¼å›¾åƒï¼›æŽ¥ç€ï¼Œæå–è®­ç»ƒæ ·æœ¬ï¼Œåˆ©ç”¨SVMå¯¹è®­ç»ƒæ ·æœ¬è¿›è¡Œè®­ç»ƒï¼Œé‡‡ç”¨è¯•éªŒæ–¹æ³•æ±‚å¾—æœ€ä¼˜æ ¸å‚æ•°å’Œæ¾å¼›å˜é‡ï¼Œæž„é€ æœ€ä¼˜SVM模型;最后,运用最优SVM模型完成矿化蚀变信息的提取。进行主成分分析时,铁染蚀变信息选择Band 1、2、3、4组合,Al-OH基团蚀变信息选择Band 1、4、6、7组合,OH和CO32-基团蚀变信息采用Band 1、2、8、9组合。在运行SVM时采用了序列最小优化算法(SMO)进行求解,速度提高了12%ã€‚å®žéªŒç»“æžœè¡¨æ˜Žï¼Œä¸Žæ³¢æ®µæ¯”å€¼æ³•ã€ä¸»æˆåˆ†åˆ†æžæ³•åŠåŸºäºŽå ‰è°±è§’å’ŒSVM的方法等3种方法相比,本文方法提取铁染蚀变信息、Al-OH基团蚀变信息及OH和CO32-基团蚀变信息的总体精度可达到87.98%、 90.01%及88.93%,Kappa系数分别为0.8011、0.8134及0.8023ï¼Œä¸ŽæˆçŸ¿åŒºå¸¦ã€å·²çŸ¥çŸ¿ç‚¹å’Œå·²æœ‰ä¸åŒåœ°è´¨èƒŒæ™¯æˆçŸ¿ç‰¹å¾ç›¸å ³æ€§è¾ƒå¥½ã€‚

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ژورنال

عنوان ژورنال: Journal of remote sensing

سال: 2021

ISSN: ['1007-4619', '2095-9494']

DOI: https://doi.org/10.11834/jrs.20219091