Image processing technics is usually applied to extract typical objects in Chemical Industry Parks(CIPs).However,its precision is considered not enough for the monitoring and management of CIPs.The pur-pose of this study is to explore the feasibility of deep learning methods in the extraction of typical objects in CIPs.This study applied convolutional neural network BASS-Net to build a typical objects recognition model of CIPs through high-resolution remote sensing images.The results showed that the overall recognition accura-cy,recall rate and F1 score of the BASS-Net model for typical objects in CIPs are 97.17%,97.76% and 97.46%,and the accuracy,recall rate and F1 for each 18 typical types can reach more than 93%,which indicat-ed that the BASS-Net trained model has the ability to classify all the typical classes in CIPs.After comparing the results with those of the RF and SVM,it can be concluded that the BASS-Net model is far superior than the other two models.The BASS-Net model can be expected to provide support for environmental monitoring and management in CIPs.
Deep learningConvolutional Neural Network(CNN)Chemical Industry Parks(CIPs)Machine learningTypical objects recognition model
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