Locality Sensitive Hashing for satellite images using texture feature vectors

This paper demonstrates the use of modified Locality Sensitive Hashing (mLSH) technique with Euclidean distance space to build a data structure for Defense Meteorological Satellite Program (DMSP) satellite imagery database that can be used to find similar satellite image matches in sublinear search time. Given the texture feature vectors of the images extracted using Gaussian central moments of wavelet edges after multi-resolution decomposition, a one-time linked-list hash table is created. A family of hash functions is drawn randomly and independently from a Gaussian distribution with mean zero and a standard deviation, d (i.e. dimensionality of the image feature vectors) to create the hash table. When tested, our algorithm has proved to be at least twenty times faster than the linear search algorithm. In addition, the algorithm ensures that the percentage of the entire database searched to find possible matches to any given query falls below ten percent.