GPU-based kernelized locality-sensitive hashing for satellite image retrieval

As the data acquisition capabilities of Earth observation (EO) satellites have been improved substantially in the past few years, large amount of high-resolution satellite images are downlinked continuously to ground stations. Such amount of data increases rapidly beyond the users´ capability to access the images´ content in reasonable time. Hence, automatic and fast interpretation of a large data volume is a computationally intensive task. Recently, approximate nearest neighbour search has been used for content-based image retrieval in sub-linear time. Kernelized locality sensitive hashing (KLSH) is a well-known approximate method, which has recently shown promising results for fast remote sensing image retrieval. This paper proposes a novel parallelization of KLSH using Graphical Processing Units (GPU), in order to perform fast parallel image retrieval. The proposed method was tested on high-dimensional feature vectors from two satellite-based image datasets, where an average speedup of 20 times was achieved.