A high-precision image classification network model based on a voting mechanism

With the development of satellite remote sensing technology, image classification task, as the basis of remote sensing data interpretation, has received wide attention to improving accuracy and robustness. At the same time, in-depth learning technology has been widely used in remote sensing and has a far-reaching impact. Since the existing image classification methods ignore the feature that the general image semantics are the same as the semantics of a single pixel, this paper presents an algorithm that uses the semantics of an image to achieve high-precision image classification. Based on the idea of partial substitution for global, this algorithm designs a split result voting mechanism and builds a Vgg-Vote network model. This mechanism votes on the semantically segmented result of an image and uses the maximum filtering function to select the category containing the most significant number of pixels as the prediction category of the image. Experiments on UC Merced Land-User complete datasets and five types of incomplete datasets with varying degrees of interference, including noise, data occlusion and loss, show that the Vote mechanism dramatically improves the classification accuracy, robustness and anti-jamming capability of Vgg-Vote.