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Multi-scale fully convolutional network-based land cover classification method for remote sensing images

With the rapid development of aviation technology, computer technology and sensor technology, remote sensing images have been widely used in transportation planning, environmental protection, fine agriculture, military monitoring and other fields. Land cover classification of remote sensing images has been a hot issue in recent years, and the early traditional methods can only extract shallow features, which is difficult to guarantee the classification accuracy. In recent years, deep learning theory has been developed rapidly with its advantage of automatically extracting deep abstract features, which has achieved great success in various fields and has also been introduced into the field of remote sensing image land cover classification. However, most of the deep learning-based land cover classification methods use local connectivity, which leads to the difficulty of simultaneous extraction of multi-scale features and a large loss of global information. In order to solve the above problems, this paper proposes a multi-scale fully convolutional network, which can accurately extract features at different scales, take into account the global feature distribution while retaining local details, and exploit the rich information contained in remote sensing images, while greatly reducing the number of parameters. Comparative experiments show that the proposed method can effectively extract the spatial distribution of farmland and houses at large scales while preserving the boundary contours of water systems and roads at small scales, which significantly improves the classification effect.