A novel locality-sensitive hashing algorithm for similarity searches on large-scale hyperspectral data

ABSTRACT Similarity search is a fundamental process in many hyperspectral remote sensing applications. In this article, we investigated the locality-sensitive hashing (LSH) algorithm for approximate similarity search on hyperspectral remote sensing data and proposed a new method that uses a regulated random hyperplane projection hash function family to index data and an adjacent graph-probing method for similarity queries. This method improves the performance of LSH over non-uniformly distributed hyperspectral data sets. Comparative experiments with three benchmark hyperspectral data sets showed that the proposed method is at least two times faster than the basic LSH and two other improved methods whilst achieving the same search accuracy. Moreover, results of the proposed method are proven equivalent to the accurate similarity search for real applications.