Abstract:

Objective: Monitoring wetland vegetation is of great significance for the protection and management of coastal wetlands. In order to improve the monitoring effect of coastal wetland vegetation, this paper attempted to propose a more effective method. Method: A part of Hangzhou Bay coastal wetland in Cixi, Ningbo was taken as the study area. Based on high spatial resolution satellite images of QuickBirds, the method of combined fully convolutional networks (FCN) and object oriented was applied to monitor coastal wetland vegetation. The images were improved the spatial resolution by integrating the multispectral data and panchromatic data, the label maps were made by the way of artificial visual interpretation, the training samples were selected with a 100×100 window, then were flipped and rotated, finally 4 904 pairs of training samples and 544 pairs of test samples were prepared. The corresponding model parameters were applied to the whole image after the FCN model training is completed with samples, and the classification results of the whole image were obtained. Multiscale segmentation was performed on the original image, and the optimal segmentation scale was determined to be 170 by using the average global score index method. The classification results of FCN were bounded by the optimal segmentation results, and the final classification results were obtained and the coastal wetland vegetation classification map was made. The confusion matrix was used to evaluate the final accuracy. Accuracy evaluation was performed on the results from FCN and the results processed by the FCN combined with the object-oriented method. Result: The overall accuracy of image classification results from the FCN is 94.39%, the accuracy of the typical wetland vegetation is more than 85%. However, there is a phenomenon of "salt and pepper", the main reason for the error is that the background of the coastal wetland is complex, and the spatial distribution of different vegetation types is chaotic. The result image after combining the two methods reduces the salt and pepper, the overall precision is 97.56%, the accuracy of typical wetland vegetation is above 90%. Conclusion: The coastal wetland vegetation monitoring method based on the FCN could effectively extract the typical wetland vegetation information from the high resolution images. On the basis of this, combined with the object-oriented multi-scale segmentation method, it could effectively eliminate the salt and pepper, make up for the defects based on pixel classification, optimize the classification results of coastal wetland vegetation, and it is worthy of promotion and application in the monitoring of coastal wetland vegetation.

Key words: coastal wetland vegetation, remote sensing monitoring, fully convolutional networks(FCN), object-oriented, high resolution