A Dual-Channel Fully Convolutional Network for Land Cover Classification Using Multifeature Information

Abstract

High-resolution remote sensing images have the advantage of timeliness, and they can display feature information in more detail. Deep learning embodies its unique characteristics in land cover classification, target recognition, and other fields, which can automatically learn the in-depth feature information of images and make accurate classification decisions. However, when deep learning models extract high-dimensional abstract feature information, they often ignore and lose part of the underlying features essential for classification accuracy. This article proposes a dual-channel fully convolutional network (D-FCN), whose two channels, respectively, take image data and low-level features such as color, texture, and shape as the different input data to combine the underlying features with high-dimensional abstract features. To reduce the complexity of the model, we add a large number of skip connections between the model and make full use of the advantages of weight sharing and local connections to connect spatial context information. We used multifeature information as the model input and compared and analyzed the impact of different features on the land cover classification accuracy, and finally obtained the most suitable combination of multifeature information. In addition, we provide a small-scale land cover classification dataset with labels to verify the applicability and transferability of the D-FCN, and use the optimal combination of multifeature information to conduct comparative experiments on the small-scale dataset. The experimental results show that D-FCN has outstanding applicability and transferability. Compared with other state-of-the-art models, D-FCN has a more challenging performance and greatly reduces model complexity.