Kernel-based hashing for content-based image retrval in large remote sensing data archive

This paper presents hashing based approximate nearest neighbor search algorithms that allow fast and accurate image retrieval in huge remote sensing data archives. Hashing methods aim at mapping high-dimensional image feature vectors into short binary codes based on hashing functions. Then, the image retrieval is accomplished according to Hamming distances of image hash codes. In particular, in this paper two hashing methods are adopted for RS image retrieval problems. The former aims at defining hash functions in the kernel space by using only unlabeled images. The latter leverages on the semantic similarity given in terms of annotated images to define much distinctive hash functions in the kernel space. The effectiveness of both methods is analyzed in terms of RS image retrieval accuracy as well as retrieval time. Experiments carried out on an archive of aerial images show that the presented hashing methods are one hundred times faster than those that exploit an exact nearest neighbor search while keeping a high retrieval accuracy.