资源简介

摘要： 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。 关键词： 非下采样小波变换;  频带特征融合;  指导学习;  样本融合;  场景分类     Abstract: 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.

相关资源

1. 水质遥感监测的关键要素叶绿素a的反演算法研究进展
2. 基于无人机遥感技术的森林参数获取研究进展
3. 基于无线传感器网络的森林生态系统观测试验平台构建
4. 基于无人机激光雷达的森林冠层高度分析
5. 多源时序国产卫星影像的森林火灾动态监测
6. 基于无人机遥感技术的台风灾害倒伏绿化树木检测

下载说明

·如果您发现该资源不能下载，请通知管理员.gissky@gmail.com

·为确保下载的资源能正常使用，请使用[WinRAR v3.8]或以上版本解压本站资源,缺省解压密码www.gissky.net ，如果是压缩文件为分卷多文件，请依次下载每一个文件，并按照顺序命名为1.rar,2.rar,3.rar...，然后鼠标右击1.rar解压.

·为了保证您快速的下载速度，我们推荐您使用[网际快车]等专业工具下载.

·站内提供的资源纯属学习交流之用,如侵犯您的版权请与我们联系.