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High-Performance Computing in Geoscience and Remote Sensing
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High-Performance Computing in Geoscience and Remote Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (20 September 2018) | Viewed by 25043

Special Issue Editors

Prof. Dr. Nicolas H. Younan

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Interests: signal processing and pattern recognition; automated target detection; image fusion; image information mining
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qian Du

grade E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Zebin Wu

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: hyperspectral image processing; remote sensing big data processing; parallel computing; machine learning; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in remote sensors with higher spectral, spatial, and temporal resolutions have significantly increased data volumes, which pose a challenge to process and analyze the resulting massive data in a timely fashion to support practical applications. Meanwhile, the development of computationally demanding deep learning techniques in data analytics also requires high performance computing. Therefore, parallel, distributed, and grid computing facilities and techniques have become indispensable tools in geoscience and remote sensing. In recent years, high-performance computing facilities and techniques have been dramatically advanced. For instance, the popular graphics processing unit (GPU) has evolved into a highly parallel many-core processor with tremendous computing power and high memory bandwidth to offer two to three orders of magnitude speedup over the CPU. This Special Issue of Sensors aims to publish the recent advances in utilizing newly high-performance computing facilities to expedite the processing and analysis of geoscience and remote sensing data for various applications. Papers are solicited in, but not limited to, the following areas:

  • High performance computing for optical, microwave, and lidar remote sensing data processing and analysis.
  • High performance computing for spaceborne, airborne, and UAV platforms.
  • High performance computing for on-board processing.
  • Recent development of high performance computing solutions for machine learning, artificial intelligence, deep learning, and big data analytics.

Dr. Nicolas Younan
Dr. Qian Du
Dr. Zebin Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • High performance computing
  • Parallel, distributed, and grid computing
  • Large-scale data processing

Published Papers (7 papers)

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Research

19 pages, 25346 KiB  
Article
A Fast Dense Feature-Matching Model for Cross-Track Pushbroom Satellite Imagery
by Wen-Liang Du, Xiao-Yi Li, Ben Ye and Xiao-Lin Tian
Sensors 2018, 18(12), 4182; https://doi.org/10.3390/s18124182 - 29 Nov 2018
Cited by 4 | Viewed by 2861
Abstract
Feature-based matching can provide high robust correspondences and it is usually invariant to image scale and rotation. Nevertheless, in remote sensing, the robust feature-matching algorithms often require costly computations for matching dense features extracted from high-resolution satellite images due to that the computational [...] Read more.
Feature-based matching can provide high robust correspondences and it is usually invariant to image scale and rotation. Nevertheless, in remote sensing, the robust feature-matching algorithms often require costly computations for matching dense features extracted from high-resolution satellite images due to that the computational complexity of conventional feature-matching model is O ( N 2 ) . For replacing the conventional feature-matching model, a fast dense (FD) feature-matching model is proposed in this paper. The FD model reduces the computational complexity to linear by splitting the global one-to-one matching into a set of local matchings based on a classic frame-based rectification method. To investigate the possibility of applying the classic frame-based method on cross-track pushbroom images, a feasibility study is given by testing the frame-based method on 2.1 million independent experiments provided by a pushbroom based feature-correspondences simulation platform. Moreover, to improve the stability of the frame-based method, a correspondence-direction-constraint algorithm is proposed for providing the most favourable seed-matches/control-points. The performances of the FD and the conventional models are evaluated on both an automatic feature-matching evaluation platform and real satellite images. The evaluation results show that, for the feature-matching algorithms which have high computational complexity, their running time for matching dense features reduces from hours level to minutes level when they are operated on the FD model. Meanwhile, based the FD method, feature-matching algorithms can achieve comparable matching results as they achieved based on the conventional model. Full article
(This article belongs to the Special Issue High-Performance Computing in Geoscience and Remote Sensing)
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19 pages, 895 KiB  
Article
High Resolution and Fast Processing of Spectral Reconstruction in Fourier Transform Imaging Spectroscopy
by Weikang Zhang, Desheng Wen, Zongxi Song, Xin Wei, Gang Liu and Zhixin Li
Sensors 2018, 18(12), 4159; https://doi.org/10.3390/s18124159 - 27 Nov 2018
Cited by 3 | Viewed by 2584
Abstract
High-resolution spectrum estimation has continually attracted great attention in spectrum reconstruction based on Fourier transform imaging spectroscopy (FTIS). In this paper, a parallel solution for interference data processing using high-resolution spectrum estimation is proposed to reconstruct the spectrum in a fast high-resolution way. [...] Read more.
High-resolution spectrum estimation has continually attracted great attention in spectrum reconstruction based on Fourier transform imaging spectroscopy (FTIS). In this paper, a parallel solution for interference data processing using high-resolution spectrum estimation is proposed to reconstruct the spectrum in a fast high-resolution way. In batch processing, we use high-performance parallel-computing on the graphics processing unit (GPU) for higher efficiency and lower operation time. In addition, a parallel processing mechanism is designed for our parallel algorithm to obtain higher performance. At the same time, other solving algorithms for the modern spectrum estimation model are introduced for discussion and comparison. We compare traditional high-resolution solving algorithms running on the central processing unit (CPU) and the parallel algorithm on the GPU for processing the interferogram. The experimental results illustrate that runtime is reduced by about 70% using our parallel solution, and the GPU has a great advantage in processing large data and accelerating applications. Full article
(This article belongs to the Special Issue High-Performance Computing in Geoscience and Remote Sensing)
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16 pages, 892 KiB  
Article
A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images
by Yi Zhang, Zebin Wu, Jin Sun, Yan Zhang, Yaoqin Zhu, Jun Liu, Qitao Zang and Antonio Plaza
Sensors 2018, 18(11), 3627; https://doi.org/10.3390/s18113627 - 25 Oct 2018
Cited by 7 | Viewed by 2779
Abstract
Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase [...] Read more.
Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA’s efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data. Full article
(This article belongs to the Special Issue High-Performance Computing in Geoscience and Remote Sensing)
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24 pages, 9788 KiB  
Article
Blind UAV Images Deblurring Based on Discriminative Networks
by Ruihua Wang, Guorui Ma, Qianqing Qin, Qiang Shi and Juntao Huang
Sensors 2018, 18(9), 2874; https://doi.org/10.3390/s18092874 - 31 Aug 2018
Cited by 10 | Viewed by 3592
Abstract
Unmanned aerial vehicles (UAVs) have become an important technology for acquiring high-resolution remote sensing images. Because most space optical imaging systems of UAVs work in environments affected by vibrations, the optical axis motion and image plane jitter caused by these vibrations easily result [...] Read more.
Unmanned aerial vehicles (UAVs) have become an important technology for acquiring high-resolution remote sensing images. Because most space optical imaging systems of UAVs work in environments affected by vibrations, the optical axis motion and image plane jitter caused by these vibrations easily result in blurring of UAV images. In the paper; we propose an advanced UAV image deblurring method based on a discriminative model comprising a classifier for blurred and sharp UAV images which is embedded into the maximum a posteriori framework as a regularization term that constantly optimizes ill-posed problem of blind image deblurring to obtain sharper UAV images. Compared with other methods, the results show that in image deblurring experiments using both simulated and real UAV images the proposed method delivers sharper images of various ground objects. Full article
(This article belongs to the Special Issue High-Performance Computing in Geoscience and Remote Sensing)
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24 pages, 8096 KiB  
Article
RPC-Based Orthorectification for Satellite Images Using FPGA
by Rongting Zhang, Guoqing Zhou, Guangyun Zhang, Xiang Zhou and Jingjin Huang
Sensors 2018, 18(8), 2511; https://doi.org/10.3390/s18082511 - 01 Aug 2018
Cited by 12 | Viewed by 4401
Abstract
Conventional rational polynomial coefficients (RPC)-based orthorectification methods are unable to satisfy the demands of timely responses to terrorist attacks and disaster rescue. To accelerate the orthorectification processing speed, we propose an on-board orthorectification method, i.e., a field-programmable gate array (FPGA)-based fixed-point (FP)-RPC orthorectification [...] Read more.
Conventional rational polynomial coefficients (RPC)-based orthorectification methods are unable to satisfy the demands of timely responses to terrorist attacks and disaster rescue. To accelerate the orthorectification processing speed, we propose an on-board orthorectification method, i.e., a field-programmable gate array (FPGA)-based fixed-point (FP)-RPC orthorectification method. The proposed RPC algorithm is first modified using fixed-point arithmetic. Then, the FP-RPC algorithm is implemented using an FPGA chip. The proposed method is divided into three main modules: a reading parameters module, a coordinate transformation module, and an interpolation module. Two datasets are applied to validate the processing speed and accuracy that are achievable. Compared to the RPC method implemented using Matlab on a personal computer, the throughputs from the proposed method and the Matlab-based RPC method are 675.67 Mpixels/s and 61,070.24 pixels/s, respectively. This means that the proposed method is approximately 11,000 times faster than the Matlab-based RPC method to process the same satellite images. Moreover, the root-mean-square errors (RMSEs) of the row coordinate (ΔI), column coordinate (ΔJ), and the distance ΔS are 0.35 pixels, 0.30 pixels, and 0.46 pixels, respectively, for the first study area; and, for the second study area, they are 0.27 pixels, 0.36 pixels, and 0.44 pixels, respectively, which satisfies the correction accuracy requirements in practice. Full article
(This article belongs to the Special Issue High-Performance Computing in Geoscience and Remote Sensing)
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12 pages, 3624 KiB  
Article
Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks
by Yuelei Xu, Mingming Zhu, Peng Xin, Shuai Li, Min Qi and Shiping Ma
Sensors 2018, 18(7), 2335; https://doi.org/10.3390/s18072335 - 18 Jul 2018
Cited by 34 | Viewed by 3678
Abstract
To address the issues encountered when using traditional airplane detection methods, including the low accuracy rate, high false alarm rate, and low detection speed due to small object sizes in aerial remote sensing images, we propose a remote sensing image airplane detection method [...] Read more.
To address the issues encountered when using traditional airplane detection methods, including the low accuracy rate, high false alarm rate, and low detection speed due to small object sizes in aerial remote sensing images, we propose a remote sensing image airplane detection method that uses multilayer feature fusion in fully convolutional neural networks. The shallow layer and deep layer features are fused at the same scale after sampling to overcome the problems of low dimensionality in the deep layer and the inadequate expression of small objects. The sizes of candidate regions are modified to fit the size of the actual airplanes in the remote sensing images. The fully connected layers are replaced with convolutional layers to reduce the network parameters and adapt to different input image sizes. The region proposal network shares convolutional layers with the detection network, which ensures high detection efficiency. The simulation results indicate that, when compared to typical airplane detection methods, the proposed method is more accurate and has a lower false alarm rate. Additionally, the detection speed is considerably faster and the method can accurately and rapidly complete airplane detection tasks in aerial remote sensing images. Full article
(This article belongs to the Special Issue High-Performance Computing in Geoscience and Remote Sensing)
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23 pages, 13957 KiB  
Article
An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks
by Ruihua Wang, Xiongwu Xiao, Bingxuan Guo, Qianqing Qin and Ruizhi Chen
Sensors 2018, 18(7), 1985; https://doi.org/10.3390/s18071985 - 21 Jun 2018
Cited by 24 | Viewed by 4084
Abstract
Unmanned aerial vehicles (UAVs) are an inexpensive platform for collecting remote sensing images, but UAV images suffer from a content loss problem caused by noise. In order to solve the noise problem of UAV images, we propose a new methods to denoise UAV [...] Read more.
Unmanned aerial vehicles (UAVs) are an inexpensive platform for collecting remote sensing images, but UAV images suffer from a content loss problem caused by noise. In order to solve the noise problem of UAV images, we propose a new methods to denoise UAV images. This paper introduces a novel deep neural network method based on generative adversarial learning to trace the mapping relationship between noisy and clean images. In our approach, perceptual reconstruction loss is used to establish a loss equation that continuously optimizes a min-max game theoretic model to obtain better UAV image denoising results. The generated denoised images by the proposed method enjoy clearer ground objects edges and more detailed textures of ground objects. In addition to the traditional comparison method, denoised UAV images and corresponding original clean UAV images were employed to perform image matching based on local features. At the same time, the classification experiment on the denoised images was also conducted to compare the denoising results of UAV images with others. The proposed method had achieved better results in these comparison experiments. Full article
(This article belongs to the Special Issue High-Performance Computing in Geoscience and Remote Sensing)
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