E. Bayro-Corrochano and E. Hancock (Eds.): CIARP 2014, LNCS 8827, pp. 1030–1038, 2014. © Springer International Publishing Switzerland 2014 An Efficient GPU-Based Implementation of the R-MSF-Algorithm for Remote Sensing Imagery David Castro-Palazuelos 1,2,* , Daniel Robles-Valdez 1 , and Deni Torres-Roman 1 1 Center for Advanced Research and Education of the National Polytechnic Institute of Mexico CINVESTAV Unidad Guadalajara, Mexico Avenida del Bosque #1145, Colonia el Bajío, 45019, Zapopan, Jalisco, México 2 Culiacan Technological Institute, Department of Electrical-Electronic Engineering, Sinaloa, Mexico dcastro@gdl.cinvestav.mx Abstract. This paper presents an efficient real time implementation of the regu- larized matched spatial filter algorithm (R-MSF-Algorithm) for remote sensing (RS) imagery that employs the robust descriptive experiment design (DED) ap- proach, using a graphics processing unit (GPU) as parallel architecture. The achieved performance is significantly greater than initial requirement of two image per second. The performance results are reported in terms of metrics as: number of operations, memory requirements, execution time, and speedup, which show the achieved improvements by the parallel version in comparison with the sequential version of the algorithm. Keywords: Remote sensing, GPU, real time implementation. 1 Introduction Currently, sensor array signal processing (SP) for imaging radars have been focus of great interest in many research works that now are available in [1, 2]. Such algorithms are computationally expensive; consequently, the majority of the sequential imple- mentations are not suitable to achieve real time or near real time. As many of the re- quired operations needed by these algorithms are: correlation, convolution, filtering, and matrix operations, parallelization technique and theory can be applied in order to improve their performances [3]. The implementation of real time systems in the field of high performance computing (HPC) is limited by the data dependencies of algo- rithms to be implemented and the features of graphic processing unit (GPU) used [4]. Hence, the advent of the GPU with user-friendly programming environments has allowed the employment of a GPU as parallel mathematical co-processor. In this doc- ument, the proposed DED framework based on the matched spatial filter (MSF) SP technique [2] are implemented (referred here as R-MSF-Algorithm). The R-MSF- Algorithm is composed of three parts: the average, the array correlation function, and the matched spatial filter (MSF). In addition, R-MSF-Algorithm is aimed to form a * Corresponding author.