Similarity Retention Loss (SRL) Based on Deep Metric Learning for Remote Sensing Image Retrieval Recently, with the rapid growth of the number of datasets with remote sensing images, it is urgent to propose an effective image retrieval method to manage and use such image data. In this paper, we propose a deep metric learning strategy based on Similarity Retention Loss (SRL) for contentbased remote sensing image retrieval. We have improved the current metric learning methods from the following aspectsxe2x80x94sample mining, network model structure and metric loss function. On the basis of redefining the hard samples and easy samples, we mine the positive and negative samples according to the size and spatial distribution of the dataset classes. At the same time, Similarity Retention Loss is proposed and the ratio of easy samples to hard samples in the class is used to assign dynamic weights to the hard samples selected in the experiment to learn the sample structure characteristics within the class. For negative samples, different weights are set based on the spatial distribution of the surrounding samples to maintain the consistency of similar structures among classes. Finally, we conduct a large number of comprehensive experiments on two remote sensing datasets with the finetuning network. The experiment results show that the method used in this paper achieves the stateoftheart performance.

Multiple Query ContentBased Image Retrieval Using Relevance Feature Weight Learning We propose a novel multiple query retrieval approach, named weightlearner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual featuredescriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the userxe2x80x99s highlevel semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.