基于频带特征融合的GL-CNN遥感图像场景分类

高分辨率卫星遥感图像场景信息的分类对影像分析和解译具有重要意义，传统的高分辨卫星遥感图像场景分类方法主要依赖于人工提取的中、低层特征且不能很好的利用图像丰富的场景信息，针对这一问题，提出一种基于频带特征融合与GL-CNN(Guided Learning Convolutional Neural Network，指导学习卷积神经网络)的分类方法。首先通过NSWT（Non-Subsampled Wavelet Transform，非下采样小波变换）提取出图像的高低频子带，将高频子带进行频带特征融合得到融合高频子带，然后联合频谱角向能量分布曲线的平稳区间分析实现融合高频子带与低频子带的样本融合，最后指导卷积神经网络自动提取图像的高低频子带包含的高层特征来实现场景分类。通过对UCM_LandUse 21类数据进行试验表明，本文方法的分类正确率达到94.52%，相比以往算法有显著提高。

Remote Sensing Image Scene Classification based on Frequency Band Feature Fusion and GL-CNN

The classification of high-resolution satellite remote sensing image scene information is of great significance for image analysis and interpretation. The traditional high-resolution satellite remote sensing image scene classification method mainly relies on the artificially extracted middle and low-level features and can not make good use of image-rich scenes. In response to this problem, a classification method based on band feature fusion and GL-CNN (Guided Learning Convolutional Neural Network) is proposed. Firstly, the high-low frequency sub-band of the image is extracted by NSWT (Non-Subsampled Wavelet Transform), and then the high-frequency sub-band is fused to obtain the fused high-frequency sub-band, and then the angular energy distribution curve is combined. The stationary interval analysis realizes the fusion of the fusion high-frequency sub-band and the low-frequency sub-band, and finally guides the convolutional neural network to automatically extract the high-level features contained in the high-low frequency sub-band of the image to realize the scene classification. Experiments on UCM_LandUse 21 data show that the classification accuracy of this method reaches 94.52%, which is significantly improved compared with previous algorithms.