Resources Science ›› 2016, Vol. 38 ›› Issue (8): 1538-1549.

• Orginal Article •

### Wetland land-cover information extraction of BP neural network based on tolerant rough set in Shuangtaizi estuarine wetland

ZHOU Linfei(), YAO Xue(), LU Xiaofeng

1. College of Water Resources,Shenyang Agricultural University,Shenyang 110161,China
• Received:2015-07-16 Revised:2015-11-19 Online:2016-08-25 Published:2016-08-25

Abstract:

Remote sensing data is the main resource for wetland monitoring because of its rich information. The BP neural network is widely used in remote sensing image classification with the characteristics of self-learning,adaptive and massively parallel processing. However,it is easy to fall into local minimum value,with slow convergence speed. A BP neural network classification method based on tolerant rough sets is put forward here,taking the Shuangtaizi estuarine wetland as the research object divided into 8 categories （water,culture pond,suaeda,reed,paddy,beach,residential land and mixed vegetation） and Landsat-8 OLI remote sensing data on 26 May 2014 as the data source. To satisfy the application demand,image preprocessing was needed including radiometric correction at the systematic level and geometric correction by ground control points and digital elevation model data for Landsat-8 data products. First,deal with sample data set collected in the study area using tolerant rough set theory preprocessing to obtain new training samples. Second,a classification model based on BP neural network was established by Matlab software platform,and land-cover information was extracted. Then it conducted evaluation for classification effect using a confusion matrix. Results show that the BP neural network based on tolerant rough set classification can eliminate noise in the training sample data,improve the success rate of training of the network and shortening the network training time. It obtained a good classification effect with an overall accuracy of 91.25%,Kappa coefficient 0.8969,increased 7.92% and 0.0926 than traditional BP neural network classification method,and 3.03% and 0.0357 higher than the pretreatment method based on rough set attribute reduction. BP neural network classification based on tolerant rough set is a preferable land-cover classification method which can help managers monitor wetland dynamics.