Affiliation:
1. College of Information and Electronic Engineering, Shangqiu Institute of Technology, Shangqiu 476000, China
Abstract
The application of remote sensing images in water body recognition has become an effective method for ecological environment detection and evaluation, which has the disadvantages of low efficiency due to the existence of interpretation marks and rich interpretation experience in the current water body environment recognition, and overreliance on human experience. In this paper, the water body recognition method is applied to remote sensing images by combining the deep convolution generation network and the combined features, which has the advantage of high recognition accuracy. In the convolutional neural network, a five-layer convolutional neural network is used to construct a remote sensing water information extraction model, the transfer learning idea is introduced, and the densely connected feature fusion structure is added, so as to achieve the purposes of accelerating the convergence speed of the neural network, reducing the requirements of the neural network on the scale of training data, and reducing the loss of spatial hierarchical information and small object information. Compared with SVM, DBN, and CNN models, the experimental results show that the recognition accuracy of the proposed method is as high as 95. 69% under the constraint of scale window, which has a wide range of application scenarios and practical significance.
Funder
Industry University Cooperation Collaborative Education Project
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Reference24 articles.
1. Application and development prospect of remote sensing technology in environmental monitoring;C. Haijian;China New Technology and New Products,2011
2. Research progress analysis of remote sensing monitoring of land remediation;J. Zhang;Journal of Agricultural Machinery,2019
3. Academic writing conference on the impact of artificial intelligence on urban planning;Editorial Department of this Journal;Journal of Urban Planning,2018
4. Road Extraction from Remote Sensing Images Using the Inner Convolution Integrated Encoder-Decoder Network and Directional Conditional Random Fields
5. Mapping of Intrusive Complex on a Small Scale Using Multi-Source Remote Sensing Images