Abstract
Recognizing land type using remote sensing images is vital for land resource management and monitoring. To improve the accuracy of land scene recognition from remote sensing images captured by sensors, an innovative classification method based on an improved capsule network was proposed. First, a circular local binary pattern algorithm extracted texture features from remote sensing images. The textured images were then fused with the original remote sensing images, and a 1-by-1 convolutional layer combined the fused feature graphs linearly. Finally, an improved capsule network performed remote sensing image scene classification. The experimental results show that the average accuracy rate of the proposed method improved by more than 6% compared with that of the typical convolutional neural network method, and the proposed approach outperformed other comparable methods in terms of accuracy. The proposed method not only provides a fast convergence speed but also improves the scene classification accuracy for remote sensing images, especially for images with rich texture information. This is helpful for land remote sensing image scene classification in various applications.
Published Version
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