High Performance Computing for Satellite Image Processing and Analyzing - A Review
---
paper_title: Recent Developments in High Performance Computing for Remote Sensing: A Review

paper_content:

Remote sensing data have become very widespread in recent years, and the exploitation of this technology has gone from developments mainly conducted by government intelligence agencies to those carried out by general users and companies. There is a great deal more to remote sensing data than meets the eye, and extracting that information turns out to be a major computational challenge. For this purpose, high performance computing (HPC) infrastructure such as clusters, distributed networks or specialized hardware devices provide important architectural developments to accelerate the computations related with information extraction in remote sensing. In this paper, we review recent advances in HPC applied to remote sensing problems; in particular, the HPC-based paradigms included in this review comprise multiprocessor systems, large-scale and heterogeneous networks of computers, grid and cloud computing environments, and hardware systems such as field programmable gate arrays (FPGAs) and graphics processing units (GPUs). Combined, these parts deliver a snapshot of the state-of-the-art and most recent developments in those areas, and offer a thoughtful perspective of the potential and emerging challenges of applying HPC paradigms to remote sensing problems.
---
paper_title: Parallel Processing Method for Airborne Laser Scanning Data Using a PC Cluster and a Virtual Grid

paper_content:

In this study, a parallel processing method using a PC cluster and a virtual grid is proposed for the fast processing of enormous amounts of airborne laser scanning (ALS) data. The method creates a raster digital surface model (DSM) by interpolating point data with inverse distance weighting (IDW), and produces a digital terrain model (DTM) by local minimum filtering of the DSM. To make a consistent comparison of performance between sequential and parallel processing approaches, the means of dealing with boundary data and of selecting interpolation centers were controlled for each processing node in parallel approach. To test the speedup, efficiency and linearity of the proposed algorithm, actual ALS data up to 134 million points were processed with a PC cluster consisting of one master node and eight slave nodes. The results showed that parallel processing provides better performance when the computational overhead, the number of processors, and the data size become large. It was verified that the proposed algorithm is a linear time operation and that the products obtained by parallel processing are identical to those produced by sequential processing.
---
paper_title: Remote sensing data fusion algorithms with parallel computing

paper_content:

The advances in satellite technologies, image analysis techniques and computational power make possible processing huge amounts of high resolution images in real time. Acquiring high resolution images has a drawback, as the pixel resolution increases the surveyed area decreases. Multispectral scene is an image stack including numerous spectral bands from the electromagnetic wave spectrum, leading to richer spectral resolution. On the other hand, higher spatial resolution is included in the Panchromatic image. In order to have an image with higher spectral and spatial resolution, the applied merging process is called fusion. In this paper, fourteen different image fusion techniques were implemented. Serial implementations of all these approaches have longer execution time disadvantage compared to parallel approaches. To decrease execution time, the methods were modified with parallel computing approaches. This paper presents a comparison regarding speed performance of all fourteen methods' serial and parallel implementations to increase pixel resolution and keep spatial resolution high by combining spectral and spatial information of high and low resolution images of the same co-registered region. Additionally, spectral quality assessments of methods are presented.
---
paper_title: A PARALLEL IMPLEMENTATION FRAMEWORK FOR REMOTELY SENSED IMAGE FUSION

paper_content:

The fusion algorithms are data-intensive and computation-intensive, whose processing performance is low if common implementation techniques are adopted. This paper presents a parallel implementation framework based on a generalized fusion model and parallel computing for remotely sensed image fusion. The steps in the implementation corresponding to each fusion algorithm are mainly the same, which discard the process steps, input and output （I/O）operations not impacting the last results. The parallel processing mechanism adopts the data decomposition strategies where different data blocks can be processed in different CPU cores. The developed software module shows that the framework can be applied to implement many algorithms which usually are divided into three general categories. The experiments show that the parallel implementation framework is high performance, which not only decreases the processing time in single CPU core environment but also efficiently utilizes the resources of multi-cores in computer.
---
paper_title: Unsupervised parallel image classification using Markovian models

paper_content:

Abstract This paper deals with the problem of unsupervised classification of images modeled by Markov random fields (MRF). If the model parameters are known then we have various methods to solve the segmentation problem (simulated annealing (SA), iterated conditional modes (ICM), etc). However, when the parameters are unknown, the problem becomes more difficult. One has to estimate the hidden label field parameters only from the observed image. Herein, we are interested in parameter estimation methods related to monogrid and hierarchical MRF models. The basic idea is similar to the expectation–maximization (EM) algorithm: we recursively look at the maximum a posteriori (MAP) estimate of the label field given the estimated parameters, then we look at the maximum likelihood (ML) estimate of the parameters given a tentative labeling obtained at the previous step. The only parameter supposed to be known is the number of classes, all the other parameters are estimated. The proposed algorithms have been implemented on a Connection Machine CM200. Comparative experiments have been performed on both noisy synthetic data and real images.
---
paper_title: An Optimized Image Mosaic Algorithm with Parallel IO and Dynamic Grouped Parallel Strategy Based on Minimal Spanning Tree

paper_content:

Image mosaic on a great number of remote sensing images with large scale is commonly I/O intensive and time-consuming. However, parallel computing is recently considered as an effective way to solve the problems of enormous computation caused by its complicated algorithm and the massive data amount. But traditional parallelization of mosaic algorithm doesn’t optimize the serial processing pass of mosaic algorithm according to the parallel strategy. Furthermore, the registration and blending in mosaic is time-consuming, but there is no effective parallel strategy of data splitting to solve this problem. And what’s more, for massive remote sensing data the problem result from the I/O performance of the mosaic algorithm will bring perform bottle. To solve these problems, An Optimized Image Mosaic Algorithm with Parallel I/O And Dynamic Grouped Parallel Strategy Based On Minimal Spanning Tree is proposed in this paper. The processing procedure of the traditional algorithm is optimized to meet the need of the parallel strategy. What’s more, effective parallel strategy of data splitting is adopted in the time-consuming part registration and blending. In addition, the multi-thread parallel I/O strategy which is overlapping I/O and computing time is adopted to speed up the algorithm efficiency. Through experimental and comparative analysis, its outstanding parallel efficiency and perfect linear speedup is showed in this paper.
---

Title: High Performance Computing for Satellite Image Processing and Analyzing - A Review

Section 1: INTRODUCTION
Description 1: This section introduces High Performance Computing (HPC) technology, its significance, and its application in remote sensing for processing and analyzing satellite images.

Section 2: PARALLEL COMPUTING
Description 2: This section elaborates on the concept of parallel computing, its types, and its utility in speeding up the processing of satellite images.

Section 3: Image fusion
Description 3: This section reviews various algorithms and methods used in image fusion through parallel processing frameworks to enhance image quality.

Section 4: Image classification
Description 4: This section discusses the significance of image classification in digital image processing, describing supervised and unsupervised classification methods along with their implementation on parallel computing systems.

Section 5: Image mosaicking
Description 5: This section highlights techniques for combining multiple images into a composite mosaic image, explaining the challenges and solutions involving parallel processing strategies.

Section 6: Morphological /neural approach for hyperspectral satellite image processing
Description 6: This section introduces parallel processing methodologies based on neural networks and morphological concepts for hyperspectral image processing.

Section 7: CLUSTER COMPUTING
Description 7: This section discusses the role of cluster computing in processing large datasets, explaining its architecture, performance, and application in remote sensing.

Section 8: DISTRIBUTED COMPUTING
Description 8: This section explains distributed computing, its working principles, benefits, and its application in remote sensing to manage large data processing efficiently.

Section 9: CONCLUSIONS & FUTURE SCOPE
Description 9: This section concludes the review by summarizing the discussed HPC techniques and suggests future directions for research and development in HPC for satellite image processing.