提出了一种基于稀疏编码和卷积神经网络的地貌场景图像分类算法;利用非下采样Contourlet变换对训练样本进行多尺度分解;在训练样本中选择图像,利用稀疏编码学习局部特征,对特征向量进行排序;选择灰度平均梯度较大的特征向量对卷积神经网络卷积核进行初始化。结果表明:所提算法可以获得比传统底层视觉特征更好的分类结果,有效避免了网络训练陷入局部最优的问题,提高了自然场景下无人机着陆地貌的分类准确率。

A landform image classification algorithm based on sparse coding and convolutional neural network is proposed. The non-subsampled Contourlet transform is applied to the training samples for multi-scale decomposition. The images are selected in the training samples to learn the local features by using sparse coding, and the feature vectors are sorted. The feature vectors with larger gray-scale mean gradients are selected to initialize the convolutional neural network convolution kernel. The results show that the proposed algorithm can obtain better classification results than traditional underlying visual features, which effectively avoids the problem of network training falling into local optimum, and improves the classification accuracy of unmanned aerial vehicles landing landform in natural scenes.