A Lightweight Model for Remote Sensing Image Retrieval with Knowledge Distillation and Mining Interclass Characteristics

There are more and more practical applications of remote sensing image retrieval in a wide variety of areas, such as land-cover analysis, ecosystem monitoring, or agriculture. It is essential to have a solution for this problem with both high accuracy and efficiency, e.g. small-sized models and low computational cost. This motivates us to propose a lightweight model for remote sensing image retrieval. We first employ interclass characteristic mining to train a cumbersome and robust model, aiming to boost the quality of retrieval results. Then, from the complex model, we apply the knowledge distillation to reduce significantly the neural network’s size. Our experiments conducted on the UC Merced Land Use dataset demonstrate the advantage of our method. Our lightweight model achieves the mAP of 0.9680 with only 3.8M parameters. This model has a higher mAP and lower number of parameters than EDML method, proposed by Cao et. al.