激光与光电子学进展, 2020, 57 (8): 081010, 网络出版: 2020-04-03
基于多特征和改进自编码的高光谱图像分类 下载: 1079次
Hyperspectral Image Classification Based on Multiple Features and an Improved Autoencoder
图像处理 高光谱图像 多特征 流形学习 自编码网络 神经网络 image processing hyperspectral image multiple features manifold learning autoencoder network neural network
摘要
针对高光谱图像特征利用不足和训练样本难以获取的问题,提出了一种具有多特征和改进堆栈稀疏自编码网络的高光谱图像分类算法。采用流形学习获得高光谱图像的低维数据结构,并提取高光谱图像的光谱特征、具有空间信息的局部二值模式(LBP)特征及拓展多属性剖面 (EMAP)特征。利用主动学习查询特征性强的未标记样本并将其标记,利用融合空谱联合信息的样本训练堆栈主动稀疏自编码神经网络并用Softmax分类器对其分类。Indian pines数据集的总体分类精度达到98.14%,Pavia U数据集总体分类精度达到97.24%。实验结果表明,该算法分类精度高,边界点分类效果更好。
Abstract
In this study, we propose a hyperspectral image classification algorithm based on multiple features and the improved stacked sparse autoencoder network to solve the problems of insufficient feature utilization and less training samples. The low-dimensional data structures of the hyperspectral images can be obtained using manifold learning, and the local binary pattern (LBP) features with spatial information and extended multi-attribute profiles (EMAP) features can be extracted from the hyperspectral images. Further, Active learning is used to query and label highly characteristic unlabeled samples. Then, the samples fusing space spectrum joint information are used to train the stacked active sparse autoencoder neural network; these samples are subsequently classified using the Softmax classifier. The overall classification accuracy of the Indian pines dataset was 98.14%, whereas the overall classification accuracy of the Pavia U dataset was 97.24%. The experimental results prove that the proposed algorithm has a high classification accuracy and can appropriately classify the boundary points.
张倩, 董安国, 宋睿. 基于多特征和改进自编码的高光谱图像分类[J]. 激光与光电子学进展, 2020, 57(8): 081010. Qian Zhang, Anguo Dong, Rui Song. Hyperspectral Image Classification Based on Multiple Features and an Improved Autoencoder[J]. Laser & Optoelectronics Progress, 2020, 57(8): 081010.