Global Descriptors of Convolution Neural Networks for Remote Scene Images Classification

Nowadays, the deep learning-based methods have been widely used in the scene-level-based image classification. However, the features automatically obtained from the last fully connected (FC) layer of single CNN without any process have little effect because of high dimensionality. In this paper, we propose a simple enhancing scene-level feature description method for remote sensing scene classification. Firstly, the principal component analysis (PCA) transformation is adopted in our research for reducing redundant dimensionality. Secondly, a new method is used to fuse features obtained by PCA transformation. Finally, the random forest classifier applying to classification makes a significant effect on compressing the training procedure. The results of experiments on the public dataset describe that feature fusion with PCA transformation performs great classification effect. Moreover, compared with the classifier softmax, the random forest classifier outperforms the softmax classifier in the training procedure.