针对现有基于阶梯网络(LN)的高光谱图像分类算法无法充分提取图像的空谱特征而导致分类精度降低的问题,提出一种基于改进阶梯网络的高光谱半监督分类算法。首先将三维卷积神经网络(3D-CNN)与长短时记忆(LSTM)网络结合,提出一种新的空谱特征提取(3D-CNN-LSTM)网络,使用该网络分步提取局部空间特征与光谱特征。然后使用3D-CNN-LSTM网络对阶梯网络的编码器与解码器进行改进,提出一种3D-CNN-LSTM-LN半监督分类算法,增强阶梯网络的特征提取能力。最后在Pavia University和Indian Pines两个数据集上对不同算法进行实验。实验结果表明,在小样本条件下,所提算法取得了最佳的分类效果,验证了所提算法具有优越性。

Aiming at the problem that the existing hyperspectral image classification algorithm based on ladder network (LN) cannot fully extract the spatial-spectral features of the image, which leads to the reduction of classification accuracy, a hyperspectral semi-supervised classification algorithm based on improved ladder network is proposed. First, the three-dimensional convolutional neural network (3D-CNN) and the long-short-term memory (LSTM) network are combined to propose a new spatial-spectral feature extraction (3D-CNN-LSTM) network, which is used to extract local spatial features step by step. Then, the 3D-CNN-LSTM network is used to improve the encoder and decoder of the ladder network, and a 3D-CNN-LSTM-LN semi-supervised classification algorithm is proposed to enhance the feature extraction ability of the ladder network. Finally, different algorithms are tested on Pavia University and Indian Pines datasets. The experimental results show that the proposed algorithm achieves the best classification effect under the condition of small samples, which verifies the superiority of the proposed algorithm.