AUTHOR=Wan Diandi , Yin Shaohua 

TITLE=Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural Network

JOURNAL=Frontiers in Ecology and Evolution

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2022.944298

DOI=10.3389/fevo.2022.944298

ISSN=2296-701X

ABSTRACT=Dongting Lake is an important lake wetland in China. How to quickly and accurately obtain the basic information of Dongting Lake ecological wetland is of great significance to the dynamic monitoring, protection and sustainable utilization of the wetland. Therefore, this paper proposes  on information extraction of Dongting Lake ecological wetland based on GA-CNN. Firstly, we know the environmental information of Dongting Lake, take Gaofen-1 image as the data source, and use normalized vegetation index and normalized water body index as auxiliary data to preprocess the change detection of remote sensing images to obtain high-precision fitting images. GA-CNN, an analysis model combining genetic algorithm (GA) and convolutional neural network (CNN), is constructed to extract the information of Dongting Lake ecological wetland efficiently, and Relu excitation function is used to improve the phenomenon of gradient disappearance and convergence fluctuation, so as to reduce the operation time. logistic regression is used for feature extraction, and finally the automatic identification and information extraction of Dongting Lake ecological wetland are realized. The research results show that the method proposed in this paper can dig the information of ground objects more deeply, express the depth features, and has high accuracy and credibility.