关键词: 遥感图像, 柯西分布, 子图像, 卷积神经网络, 空间金字塔池化

Abstract:

The classification of High-Resolution Remote Sensing images（HRRS） is a challenging task. Aiming at the semantic characteristics of remote sensing dataset images, a Convolutional Neural Network（CNN） with random sub-image extraction of the dataset images and a pyramid pooling model is proposed. The size of the input image is cut based on the random size of Cauchy distribution, and these sub-images with different sizes but the same label are sent to a convolutional neural network with Spatial Pyramid Pooling（SPP）. The predicted category mode is output as the final classification. The experimental results show that the proposed method improves the classification accuracy of multi-class remote sensing images.

Key words: remote sensing image, Cauchy distribution, sub-image, convolutional neural network, space pyramid pooling