Partial Randomness Hashing for Large-Scale Remote Sensing Image Retrieval

Abstract

With the rapid progress of satellite and aerial vehicle technologies, large-scale remote sensing (RS) image retrieval has recently become an important research issue in geosciences. Hashing-based searching approaches have been widely employed in content-based image retrieval tasks. However, most hash schemes compromise between learning efficiency and retrieval accuracy, and can thus barely satisfy the precise requirements in RS data analysis. To address these shortcomings, we introduce a partial randomness scheme for learning hash functions, which is referred to as partial randomness hashing (PRH). Specifically, for constructing hash functions, a part of model parameter values are randomly generated and the remaining ones are trained based on RS images. The randomness enables an efficient hash function construction and the trained model parameters encode characteristics from RS images. The coplay between random and trained model parameters results in both efficient and effective learning scheme for constructing hash functions. Experiments on two large public RS image data sets have shown that our PRH method outperforms state of the arts in terms of both learning efficiency and retrieval accuracy.