Study And Implementation Of Parallel Algorithms For Remote Sensing Image Processing

With the fast development of computer technology and extending of application, digital image processing is faced with challenges of pursuing high speed and increasing complexity, in which High Performance Computing (HPC), especially parallel computing acts as a very important role. Remote Sensing (RS), as a rising synthetic subject of technology, has got a big attention whether in civil or military application area. For the quickly changing and updating of remote sensors, the huge data and complicated information that RS brings to us is unprecedented. On this background, as an application of digital image processing in RS area, RS image processing is combined with HPC naturally and imminently. The projects discussed in this paper are outcome of this combination.According to processing levels of ordinary digital image, RS image processing is described from four concept levels including pre-processing level, data processing level, information extracting level and knowledge level. For the sake of satellite image, common workflow of RS image processing is summarized first, and then based on parallel computing techniques three types of algorithms from the first three levels are lucubrated in this paper.Preprocessing is a necessary step during processing RS images, in which geometric correction is a basic course with dense and complicated computing. To solve the problems of traditional parallel method such as bad programmability and large communication penalty, we innovatively propose and realize an efficient parallel algorithm of systematically geometric correction (PGCA) by learning the flow of systematically geometric correction and analyzing its parallelism. In our algorithm no communication is needed during the course of resampling by calculating local irregular area. As a result, required communication is delayed, and its cost could be minimized by use of parallel data regularization in local memory. Experimental results show that our work is significant to improving job efficiency of RS ground station and directing hardware design of runtime system on satellites.Image registration is widely used in RS applications. With the increasing importance of multi-platform RS missions, existing registration techniques have become the bottleneck, when dealing with great RS data. Our work focuses on automatic registration technology for RS images and its efficient parallel algorithms. We classify the automatic registration algorithms into two types of point-to point matching and global registration. And by utilizing the advantages of these two kinds of methods, a synthetic solution is proposed. According to this solution, a wavelet-based global registration algorithm (WAGR) and a subimage-based automatic point-to-point matching algorithm are designed. To the former, a new iteratively refining strategy is designed in detail to reduce the search space; moreover, four parallel solutions are presented for it. In addition, the natures and applicability of these four parallel algorithms are analyzed, which makes a quantitative criterion to choose best solution indifferent conditions. While, to the latter, we explore the methods to automatic extract control points (CPs). Our algorithm not only successfully selects CPs with important features, but also makes these CPs distributed evenly in image. The conclusions draw from this part work is meaningful to RS applications theoretically and practically.Image segmentation is being studied by researchers all along, and also is a hotspot in RS image processing area. Watershed transform is a classical and effective method of image segmentation in mathematical morphology. This method, with a wide perspective, has been applied successively into RS images processing of satellite and radar. Nevertheless classical watershed algorithms have strong recursive nature, so straightforward parallel ones have a very low efficiency, which is a big obstacle for wide use of watershed algorithm. In this paper, serial and parallel watershed-based segmentation algorithms are discussed thoroughly. First of all, r...