A multi-level improved circle pooling for scene classification of high-resolution remote sensing imagery.

Scene classification of high-spatial resolution imagery (HSRI) includes various potential applications in various fields. Recently, deep convolutional neural networks (CNNs) have achieved competitive performance as a result of the powerful capability of feature extraction. In this paper, we propose a multi-level improved circle pooling (MICP) method with the pre-trained CNN-based model to enhance the discriminative power of CNN activations for scene classification. Specifically, an improved pooling strategy is presented to generate annular subregions without padding operations in traditional concentric circle pooling. Then, we extract the pooling features in these subregions under different levels and build a holistic representation by fusing these multi-level features. MICP is an effective and simple strategy enriching rotation insensitivity and multiscale spatial information. According to the experiments conducted on three challenging HRSI scene data sets, the proposed pooling method achieves similar or better classification accuracy compared to the other CNN-based scene classification methods. Moreover, a comprehensive discussion regarding the effect of data augmentation reveals that the proposed method can enhance the rotation insensitivity of CNNs for the HRSI scene classification. [ABSTRACT FROM AUTHOR]