On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery
On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery
复制标题
DOI：
10.1080/2150704x.2015.1062157
复制
发表时间：
2015-07
影响因子：
3.4
通讯作者：
Wenzhi Zhao;Zhou Guo;Jun Yue;Xiuyuan Zhang;Liqun Luo
中科院分区：
文献类型：
--
作者：
Wenzhi Zhao;Zhou Guo;Jun Yue;Xiuyuan Zhang;Liqun Luo
近年来，卫星图像在空间和光谱分辨率方面都有了很大的提高。在高度发达的遥感图像的主要未解决的问题之一是手动选择和适当的功能组合，根据光谱和空间特性。深度学习框架可以从训练数据集中自动学习全局和鲁棒的特征，并且在不同的图像分类任务中达到了最先进的分类精度。在这项研究中，提出了一种技术，试图通过结合深度学习特征来对高光谱图像进行分类。首先，通过多尺度卷积自动编码器提取深度学习特征。然后，基于学习的深度学习特征，训练逻辑回归分类器进行分类。最后，分析了深度学习框架的参数，并介绍了潜在的发展。利用反射式光学系统成像光谱仪传感器采集的Pavia数据集进行了实验。研究发现，基于深度学习的方法比传统方法提供了更准确的分类结果。
In recent years, satellite imagery has greatly improved in both spatial and spectral resolution. One of the major unsolved problems in highly developed remote sensing imagery is the manual selection and combination of appropriate features according to spectral and spatial properties. Deep learning framework can learn global and robust features from the training data set automatically, and it has achieved state-of-the-art classification accuracies over different image classification tasks. In this study, a technique is proposed which attempts to classify hyperspectral imagery by incorporating deep learning features. Firstly, deep learning features are extracted by multiscale convolutional auto-encoder. Then, based on the learned deep learning features, a logistic regression classifier is trained for classification. Finally, parameters of deep learning framework are analysed and the potential development is introduced. Experiments are conducted on the well-known Pavia data set which is acquired by the reflective optics system imaging spectrometer sensor. It is found that the deep learning-based method provides a more accurate classification result than the traditional ones.