Region-based cascade pooling of convolutional features for HRRS image retrieval

ABSTRACT High-resolution remote sensing (HRRS) images contain abundant and complex visual contents. It is very important to extract powerful features to represent the complex contents of HRRS images in the image retrieval. This letter proposes a region-based cascade pooling (RBCP) method to aggregate convolutional features from both the pre-trained and the fine-tuned convolutional neural networks (CNNs). The RBCP method adopts small pooling regions, and first uses max-pooling on the feature maps of the last pooling layer, then employs average-pooling on the max-pooled feature maps. Furthermore, the feature map size is related to the input size, then two kinds of input sizes (required input size and original input size) are compared and analyzed for the RBCP features. The simulation results show that the RBCP features perform better than the features with traditional pooling methods, since multiple patch features can be extracted to describe the details of HRRS images. The RBCP method combines the advantages of max-pooling and average-pooling to extract discriminative features, thus it provides competitive results compared with state-of-the-art methods.