Land cover classification of Yellow River estuary wetland using multiscale feature extraction network

The development of remote sensing technology has made large-scale and long-term observations possible, but extracting land cover distribution from complex remote sensing data is full of challenges. Convolutional neural networks (CNNs) have played an important role in remote sensing data processing, but their performance is still limited by the ability to combine spatial-spectral information and extract discriminative features. To address these issues, a multiscale feature extraction network (MFENet) was designed for land cover classification. Firstly, multiple multiscale feature extraction layers are used to mine deep spatial-spectral features. Secondly, the gated feature enhancement (GFE) module is employed to further enhance the non-linear feature expression ability. Finally, the joint optimization of the classification model using cross-entropy loss and feature discrimination loss achieves inter-class feature differentiation. Extensive experiments over four wetland datasets demonstrate the superiority of the proposed MFENet compared with several classification technologies.