Hybrid feature selection for cropland identification using GF-5 satellite image

نویسندگان

چکیده

ç²¾å‡†å†œç”°è¯†åˆ«æ˜¯å†œä½œç‰©ä¼°äº§å’Œç²®é£Ÿå®‰å ¨è¯„ä¼°çš„åŸºç¡€ã€‚é¥æ„Ÿæ•°æ®ä½œä¸ºå†œç”°è¯†åˆ«çš„é‡è¦æ•°æ®æºï¼Œå¯æä¾›åŠ¨æ€ã€å¿«é€Ÿçš„ç›‘æµ‹ç»“æžœã€‚é«˜å ‰è°±æ•°æ®åœ¨å†œç”°è¯†åˆ«åˆ†ç±»æ–¹é¢å ·æœ‰å·¨å¤§çš„åº”ç”¨æ½œåŠ›ï¼Œä½†å ¶ä¸­çš„å†—ä½™æ³¢æ®µå½±å“äº†åˆ†ç±»æ•ˆçŽ‡å’Œåˆ†ç±»ç²¾åº¦ã€‚å› æ­¤ï¼Œæœ¬ç ”ç©¶æå‡ºäº†ä¸€ç§é€‚ç”¨äºŽé«˜å ‰è°±æ•°æ®å†œç”°åˆ†ç±»çš„æ··åˆå¼ç‰¹å¾é€‰æ‹©ç®—æ³•ã€‚é¦–å ˆï¼ŒåŸºäºŽå˜é‡çš„é‡è¦æ€§æŽ’åºæˆ–çº¦æŸç¨‹åº¦ï¼ŒæŒ‰æ­¥é•¿é€æ­¥è¿›è¡Œé™ç»´ï¼›å ¶æ¬¡ï¼Œå¯»æ‰¾åˆ†ç±»ç²¾åº¦éª¤å‡çš„è½¬æŠ˜ç‚¹ï¼Œå¹¶å°†å ¶å¯¹åº”çš„å˜é‡ä½œä¸ºç‰¹å¾å­é›†ï¼›æœ€åŽï¼Œåˆ©ç”¨åºåˆ—åŽå‘é€‰æ‹©SBS(Sequential Backward Selectionï¼‰æ–¹æ³•æœç´¢æœ€ä¼˜åˆ†ç±»ç‰¹å¾å­é›†ã€‚æœ¬ç ”ç©¶åˆ©ç”¨GF-5é«˜å ‰è°±æ•°æ®ï¼Œå ±ç ”ç©¶äº†3种降维方法(随机森林RF(Random Forest)、互信息MI(Multi-Information)和L1正则化(L1 regularization))和3种分类算法(随机森林、支持向量机SVM(Support Vector Machine)和K近邻KNN(K-Nearest Neighbor))的组合在农田分类中的表现。结果表明,基于L1æ­£åˆ™åŒ–æ³•å¾—åˆ°çš„ç‰¹å¾å­é›†è‡ªç›¸å ³æ€§è¾ƒä½Žï¼Œå¹¶ä¸”åŒ å«çš„çº¢è¾¹å’Œè¿‘çº¢å¤–æ³¢æ®µæœ‰æ•ˆæé«˜äº†å†œç”°ã€æ£®æž—å’Œè£¸åœŸçš„åŒºåˆ†åº¦ã€‚åœ¨ä¸åŒåˆ†ç±»æ¨¡åž‹æ¯”è¾ƒä¸­å‘çŽ°ï¼ŒSVM在高维空间中表现出非常好的抗噪能力,分类精度高于RF和KNN。而RF在低维空间中的泛化能力要高于SVM和KNN。相比于第一步降维得到的特征子集,使用SBSæœç´¢å¾—åˆ°çš„æœ€ä¼˜ç‰¹å¾å­é›†å‡æé«˜äº†åˆ†ç±»ç²¾åº¦ã€‚æœ€ç»ˆï¼Œå ·æœ‰23ç»´è¾“å ¥çš„L1-SVM-SBS分类模型得到了最高的总体分类精度(94.64%)和农田召回率(95.83%ï¼‰ã€‚æœ¬ç ”ç©¶ä¸ºé«˜å ‰è°±æ•°æ®ç‰¹å¾ä¼˜é€‰æä¾›äº†ä¸€ç§æ–°æ€è·¯ï¼Œç­›é€‰å‡ºäº†æ›´å ·ä»£è¡¨æ€§çš„ç‰¹å¾æ³¢æ®µï¼Œæé«˜äº†å†œç”°åˆ†ç±»ç²¾åº¦ï¼Œå¯¹é«˜å ‰è°±é¥æ„Ÿåˆ†ç±»ç ”ç©¶å ·æœ‰å‚è€ƒä»·å€¼ã€‚

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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.20220458