基于改进残差网络的高光谱图像分类算法研究

Research on Hyperspectral Image Classification Algorithm Based on Improved Residual Network

针对高光谱图像在训练样本较少时无法构建有效的深度学习分类模型的问题,对传统的残差块(residual block)模型进行改进,提出一种“金字塔”残差块分类算法。设计一种包含数十个卷积层的深度学习网络,与传统残差模型相比大大减少了参数量,且可以充分提取高光谱图像的深层空间-光谱特征;在2种开源数据集Indian Pines和University of Pavia上进行实验,同时选取了3种经典的分类方法作为对比。实验结果表明:该算法表现效果最佳,可以有效提高高光谱图像的分类精度。

Aiming at the problem that it is impossible to construct an effective deep learning classification model for hyperspectral images when there are few training samples,a“pyramid”residual block classification algorithm is proposed by improving the traditional residual block model.A deep learning network containing dozens of convolution layers is designed,which greatly reduces the number of parameters compared with the traditional residual model,and can fully extract the deep spatial-spectral features of hyperspectral images;Experiments are carried out on two open source datasets Indian Pines and University of Pavia,and three classical classification methods are selected for comparison.The experimental results show that the algorithm has the best performance and can effectively improve the classification accuracy of hyperspectral images.