针对如何充分利用空间特征来达到较高的高光谱图像分类精度的问题，提出了一种基于三维离散小波变换(3D-DWT)与随机补丁网络(RPNet)结合的高光谱图像的地物属性分类算法。在分类过程中，综合3D-DWT提取的特征和RPNet深度学习框架提取的特征，利用支持向量机(SVM)对特征向量进行分类。所提出的方法在Indian Pines和University of Pavia两个数据集上进行测试，结果表明该方法比现有方法有显著的分类性能的提高。
Hyperspectral image classification algorithm based on three-dimensional wavelet transform and random patches network
Aiming at the problem of how to make full use of spatial features to achieve high precision,a hyperspectral image classification algorithm based on 3D discrete wavelet transform(3D-DWT)and random patches network(RPNet)was proposed.During the classification process,the features extracted from 3D-DWT and RPNet deep learning framework were integrated,and support vector machines(SVM)were used to classify the feature vectors.The method proposed was tested on Indian Pines and University of Pavia datasets.The results showed that the method has significantly better performance than the existing methods.
3D discrete wavelet transformrandom patches networksupport vector machinehyper-spectral image classification
万方数据
维普
