Remote Sensing Image Retrieval Based on DenseNet Model and CBAM
Yongmei Zhang, Min Gang Xu, Xiaodong Li · 2020
As a retrieval method, Hash is widely used in the field of image retrieval because it can meet the retrieval time requirements for large-scale image retrieval. In order to solve the problem that most of the current image retrieval models are weak in representing remote sensing images, especially for remote sensing images with complex background, it is easy to confuse the background with the image subject. This paper proposes a remote sensing image retrieval model based on DenseNet. On the ground of original DenseNet model, the method in this paper adds CBAM to each Dense Block of the model, extracts the image features with regional attention and channel attention, and adopts the triple loss function to achieve the retrieve of remote sensing images. The experiment results show the retrieval accuracy of this presented method is significantly improved based on global features in the common datasets of CIFAR-10 and NUS-WIDE as well as the remote sensing image dataset of UC-Merced compared with the existing retrieval methods.