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With the rapid growth of remote sensing image data, it has become necessary to effectively and efficiently retrieve images from a big image database for managing and exploiting such data. This paper presents a novel method for content-based remote sensing image retrieval (CBRSIR) that re-ranks the initial retrieval result using two image-to-class distances, which are the similarity between an image and an image class. One is the image-to-training-class distance between an image and each image class of the training dataset, which uses the information included in the train samples with known-label. It is used to calculate the weight of the image classes and estimating the class of an image. The other is the image-to-query-class distance between an image and the query class that is obtained using the k-nearest neighbor ( $k$ NN) method. The proposed method first obtains the initial retrieval results via sorting the distances of the CNN feature between the query image and each retrieved image in ascending order. Then, the image-to-query-class distance is calculated according to the initial result, and the weight of each class is calculated according to the class probability of images obtained by CNN and the image-to-training-class distances. Finally, the initial result is re-ranked to get the final retrieval result via the weighted image-to-class distances. The performance of the proposed method is tested on UCMD and PatternNet databases. Experimental results show the effectiveness of the proposed re-ranking scheme in image retrieval in comparison to other state-of-the-art techniques.

A New Re-Ranking Method Based on Convolutional Neural Network and Two Image-to-Class Distances for Remote Sensing Image Retrieval