Remote Sensing Image Scene Oriented Convolutional Neural Network Recursive Recognition Model
He Haiqing1,2,3，Pang Yan1,2，Chen Xiaoyong1,2
(1.School of Geomatics，East China University of Technology，Nanchang 330013，China；
2.Key Laboratory of Watershed Ecology and Geographical Environment Monitoring，NASG，Nanchang 330013，China；
3.School of Resource and Environmental Sciences，Wuhan University，Wuhan 430079，China)
Abstract：In order to solve low separability and rough details in scene recognition，remote sensing image scene oriented convolutional neural network recursive recognition model is presented.Firstly，deep convolutional neural network with multi\|convolutional layers and multi\|pooling layers is constructed by multi\|resolution scenes.Then quad\|grids are subdivided to DCNN scene recursive recognition based on Confusion Index (CI)by softmax probability，and multi\|sliding windows are used to tune recursively for accurately locating scene targets.Experimental results show that the proposed model can adapt scene recognition with different scale，and significantly improve the accuracy compared with the commonly used DCNN.