Remote Sensing Image Retrieval Using Convolutional Neural Network Features and Weighted Distance

Abstract

Remote sensing image retrieval (RSIR) is a fundamental task in remote sensing. Most content-based RSIR approaches take a simple distance as similarity criteria. A retrieval method based on weighted distance and basic features of convolutional neural network (CNN) is proposed in this letter. The method contains two stages. First, in offline stage, the pretrained CNN is fine-tuned by some labeled images from the target data set, then used to extract CNN features, and labeled the images in the retrieval data set. Second, in online stage, we use the fine-tuned CNN model to extract the CNN feature of the query image and calculate the weight of each image class and apply them to calculate the distance between the query image and the retrieved images. Experiments are conducted on two RSIR data sets. Compared with the state-of-the-art methods, the proposed method is simplified but efficient, significantly improving retrieval performance.