上海国土资源
上海國土資源
상해국토자원
SHANGHAI LAND&RESOURCES
2015年
3期
95-97
,共3页
遥感影像%城市灾害监测%雾区识别%BP神经网络
遙感影像%城市災害鑑測%霧區識彆%BP神經網絡
요감영상%성시재해감측%무구식별%BP신경망락
remote sensing image%urban disaster monitoring%heavy fog recognition%back propagation (BP) neural network
使用改进算法构造BP神经网络，利用MATLAB中train函数训练，并用sim函数进行仿真，达到提取遥感影像中雾区的目的。图像处理结果表明，BP神经网络方法可以较好地分离影像中的雾区与其他地物。
使用改進算法構造BP神經網絡，利用MATLAB中train函數訓練，併用sim函數進行倣真，達到提取遙感影像中霧區的目的。圖像處理結果錶明，BP神經網絡方法可以較好地分離影像中的霧區與其他地物。
사용개진산법구조BP신경망락，이용MATLAB중train함수훈련，병용sim함수진행방진，체도제취요감영상중무구적목적。도상처리결과표명，BP신경망락방법가이교호지분리영상중적무구여기타지물。
The recognition and separation of cloud and heavy fog has been a particularly chalenging aspect of weather forecasting. Recently, on account of rapid socio-economic development, the harmful effects of fog have become increasingly serious, and some fog events have been classiifed as natural disasters. Thus, to prevent fog disasters, the monitoring of heavy fog and the development of early warning systems for heavy fog have become a focus of academic research. This study used an improved back propagation (BP) algorithm to build a BP neural network, using a train function to train the net and uses sim functions to simulate the net. In this way, areas of fog can be identiifed in remote sensing images. Experimental results show that the BP network can properly separate areas of fog from other meteorological features, thus producing good results in terms of prediction and early warning of conditions conducive to heavy fog.
