Paper · 2024 · National Remote Sensing Bulletin

Transferring deep convolutional neural network models for generalization mapping of autumn crops

ZHANG Feng; ZHANG Jinshui; DUAN Yaming; et al.

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Abstract

深度卷积神经网络DCNN(Deep Convolutional Neural Networksï¼‰å ·æœ‰â€œç«¯åˆ°ç«¯â€ã€é«˜æ•ˆæå–å¤šå°ºåº¦è¯­ä¹‰ç‰¹å¾çš„ä¼˜ç‚¹ï¼Œè¢«å¹¿æ³›åº”ç”¨äºŽé¥æ„Ÿå†œä½œç‰©è¯†åˆ«ä¸­ï¼Œå–å¾—äº†é‡è¦çš„è¿›å±•ã€‚ä½†æ·±åº¦å­¦ä¹ æ¨¡åž‹éœ€è¦å¤§é‡æ ‡ç­¾æ ·æœ¬ï¼ŒèŽ·å–è´¹æ—¶è´¹åŠ›ã€æˆæœ¬é«˜ï¼Œé™åˆ¶äº†å ¶å¹¿æ³›ä½¿ç”¨ã€‚æœ¬æ–‡åŸºäºŽæ¨¡åž‹è¿ç§»å­¦ä¹ ç­–ç•¥ï¼Œå°†ç¾Žå›½å†œä¸šéƒ¨ç»Ÿè®¡ç½²ä½œç‰©æ•°æ®å±‚CDL(Cropland Data Layerï¼‰ä½œä¸ºä½œç‰©æ ‡ç­¾æ•°æ®ï¼Œåˆ©ç”¨ä¼Šåˆ©è¯ºä¼Šå·žå’Œå°ç¬¬å®‰å·žçš„ä½œç‰©ç”Ÿé•¿å­£Landsat OLI卫星影像训练2016年—2019å¹´4个单年份模型和2016年—2017年、2016年—2018年两个多年份的U-net模型,将模型直接在美国3个测试区和中国黑龙江省黑河市的2016年—2020å¹´è¿ç§»æ³›åŒ–åˆ†ç±»ã€‚ç ”ç©¶ç»“æžœè¡¨æ˜Žï¼š1)基于CDL训练的U-net模型能够在美国本土推广应用,美国3个测试区的2016年—2020年的时间泛化总体精度在80%ä»¥ä¸Šï¼ŒçŽ‰ç±³è¯†åˆ«ç²¾åº¦é«˜äºŽå¤§è±†ï¼Œè¯´æ˜Žäº†æ¨¡åž‹åœ¨ç©ºé—´ä¸Šå ·æœ‰å¾ˆå¥½çš„è¿ç§»èƒ½åŠ›ã€‚ï¼ˆ2)对于黑河市秋粮,多年玉米识别总体精度平均高出大豆3%ï¼ŒåŽŸå› æ˜¯çŽ‰ç±³ç§æ¤åœ°å—æ›´ä¸ºè§„æ•´ã€ç§æ¤è§„æ¨¡æ›´å¤§ï¼Œå•å¹´ä»½ç§‹ç²®è¯†åˆ«æ€»ä½“ç²¾åº¦69%—79%ï¼Œä½†å¤šå¹´ä»½æ¨¡åž‹è¦ä¼˜äºŽå•å¹´ä»½æ¨¡åž‹ï¼Œè¿™å¯èƒ½æ˜¯éšæ ‡ç­¾æ ·æœ¬æ•°é‡å¢žå¤šæå‡äº†è®­ç»ƒæ ·æœ¬çš„ä»£è¡¨æ€§ï¼Œå°†ä¸­å›½ä¸Žç¾Žå›½ç§‹ç²®ç§æ¤çš„å·®å¼‚é€šè¿‡æ ·æœ¬æ•°é‡çš„å¢žåŠ å¾—ä»¥å¼¥è¡¥ï¼Œæ¨¡åž‹è¿ç§»åˆ°ä¸­å›½é»‘æ²³åŒºåŸŸçš„ç²¾åº¦å‡ä½ŽäºŽç¾Žå›½æœ¬åœŸï¼Œè¿™æ˜¯ç”±äºŽæ´²é™ é—´æ°”å€™ã€ä½œç‰©ç‰©å€™ã€ç§æ¤å†œä¸šæ™¯è§‚ç­‰å·®å¼‚å¯¼è‡´é¥æ„Ÿç‰¹å¾ä¸ä¸€è‡´ï¼Œå¯¼è‡´æ¨¡åž‹æ³›åŒ–èƒ½åŠ›é™ä½Žã€‚

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