基于三维卷积神经网络的高光谱图像特征提取与分类

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2018-05-25 DOI:10.1109/DDCLS.2018.8515930

Xuefeng Liu, Qiaoqiao Sun, Y. Meng, Congcong Wang, Min Fu

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引用次数: 6

摘要

深度学习在高光谱图像(HSI)分类方面具有巨大的潜力。为了充分利用HSI中的信息，提高分类精度，提出了一种新的基于3d -卷积神经网络(3D-CNN)的分类方法。同时，为了解决HSI样本不足的问题，引入了虚拟样本。实验结果表明，该方法在HSI分类中具有良好的应用前景。

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Feature Extraction and Classification of Hyperspectral Image Based on 3D- Convolution Neural Network

Deep learning has huge potential for hyperspectral image (HSI) classification. In order to fully exploit the information in HSI and improve the classification accuracy, a new classification method based on 3D-convolutional neural network (3D-CNN) is proposed. In the meantime, virtual samples are introduced to solve the problem of insufficient samples of HSI. The experimental results show that the proposed method has a good application prospect in HSI classification.

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2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)

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