改进的基于深度学习的遥感图像分类算法

针对传统的基于深度学习的遥感图像分类算法未能有效融合多种深度学习特征,且分类器性能欠佳的问题,提出一种改进的基于深度学习的高分辨率遥感图像分类算法。首先,设计并搭建一个七层卷积神经网络;其次,将高分辨率遥感图像样本输入到该网络中进行网络训练,得到最后两个全连接层输出作为遥感图像两种不同的高层特征;再次,针对该网络第五层池化层输出,采用主成分分析(PCA)进行降维,作为遥感图像的第三种高层特征;然后,将上述三种高层特征通过串联的形式进行融合,得到一种有效的基于深度学习的遥感图像特征;最后,设计了一种基于逻辑回归的遥感图像分类器,可以对遥感图像进行有效分类。与传统基于深度学习的遥感图像分类算法相比,所提算法分类准确率有较高提升。实验结果表明,该算法在分类准确率、误分类率和Kappa系数上表现优异,能实现良好的分类效果。

Improved remote sensing image classification algorithm based on deep learning

In order to solve the problem that the traditional deep learning based remote sensing image classification algorithms cannot effectively fuse multiple deep learning features and their classifiers have poor performance, an improved high-resolution remote sensing image classification algorithm based on deep learning was proposed. Firstly, a seven-layer convolutional neural network was designed and constructed. Secondly, the high-resolution remote sensing images were input into the network to train it, and the last two fully connected layer outputs were taken as two different high-level features for the remote sensing images. Thirdly, Principal Component Analysis (PCA) was applied to the output of the fifth pooling layer in the network, and the obtained dimensionality reduction result was taken as the third high-level features for the remote sensing images. Fourthly, the above three kinds of features were concatenated to get an effective deep learning based remote sensing image feature. Finally, a logical regression based classifier was designed for remote sensing image classification. Compared with the traditional deep learning algorithms, the accuracy of the proposed algorithm was increased. The experimental results show that the proposed algorithm performs excellent in terms of classification accuracy, misclassification rate and Kappa coefficient, and achieves good classification results.