GPU-Based Normalized Compression Distance for Satellite Images

Abstract

The measurement of the normalized compression distance (NCD) between two objects is computed by counting the number of common patterns that appear in the representation of compressed objects. Classical Lempel-Ziv (LZ) algorithms do not achieve high compression speeds on images because they require sequentially traversing the linearized version of object to build the corresponding dictionary, i.e., dynamic approach to optimize the encoding based upon the particular input. This paper proposes a faster approach for computing the NCD metric in a parallel manner by estimating compressions on GPU and multi-thread schemes. The goal of calculating distances is to use them in clustering and classification of input images. The tests are run on the UC Merced Land Use and BigEarthNet datasets, which are both well-known and frequently used in the field of remote sensing.