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Remote Sensing
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High Performance Computing in Remote Sensing
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High Performance Computing in Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 April 2013) | Viewed by 44014

Special Issue Editor

Dr. Prasad S. Thenkabail

E-Mail Website
Guest Editor
Senior Scientist (ST), U. S. Geological Survey (USGS), USGS Western Geographic Science Center (WGSC), 2255, N. Gemini Dr., Flagstaff, AZ 86001, USA
Interests: hyperspectral remote sensing, remote sensing expertise in a number of areas including: (a) global croplands, (b) agriculture, (c) water resources, (d) wetlands, (e) droughts, (f) land use/land cover, (g) forestry, (h) natural resources management, (i) environments, (j) vegetation, and (k) characterization of large river basins and deltas
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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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. Remote Sensing 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 2700 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
  • remote sensing
  • parallel, distributed and grid computing
  • GPU computing

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Published Papers (4 papers)

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Research

1619 KiB  
Article
Geo-Correction of High-Resolution Imagery Using Fast Template Matching on a GPU in Emergency Mapping Contexts
by Guido Lemoine and Martina Giovalli
Remote Sens. 2013, 5(9), 4488-4502; https://doi.org/10.3390/rs5094488 - 12 Sep 2013
Cited by 10 | Viewed by 9228
Abstract
The increasing availability of satellite imagery acquired by existing and new sensors allows a wide variety of new applications that depend on the use of diverse spectral and spatial resolution data sets. One of the pre-conditions for the use of hybrid image data [...] Read more.
The increasing availability of satellite imagery acquired by existing and new sensors allows a wide variety of new applications that depend on the use of diverse spectral and spatial resolution data sets. One of the pre-conditions for the use of hybrid image data sets is a consistent geo-correction capacity. We demonstrate how a novel fast template matching approach implemented on a graphics processing unit (GPU) allows us to accurately and rapidly geo-correct imagery in an automated way. The key difference with existing geo-correction approaches, which do not use a GPU, is the possibility to match large source image segments (8,192 by 8,192 pixels) with relatively large templates (512 by 512 pixels) significantly faster. Our approach is sufficiently robust to allow for the use of various reference data sources. The need for accelerated processing is relevant in our application context, which relates to mapping activities in the European Copernicus emergency management service. Our new method is demonstrated over an area northwest of Valencia (Spain) for a large forest fire event in July 2012. We use the Disaster Monitoring Constellation’s (DMC) DEIMOS-1 and RapidEye imagery for the delineation of burnt scar extent. Automated geo-correction of each full resolution image set takes approximately one minute. The reference templates are taken from the TerraColor data set and the Spanish national ortho-imagery database, through the use of dedicated web map services. Geo-correction results are compared to the vector sets derived in the Copernicus emergency service activation request. Full article
(This article belongs to the Special Issue High Performance Computing in Remote Sensing)
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4706 KiB  
Article
Harmonization of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from Sea-ViewingWide Field-of-View Sensor (SeaWiFS) and Medium Resolution Imaging Spectrometer Instrument (MERIS)
by Guido Ceccherini, Nadine Gobron and Monica Robustelli
Remote Sens. 2013, 5(7), 3357-3376; https://doi.org/10.3390/rs5073357 - 12 Jul 2013
Cited by 6 | Viewed by 8410
Abstract
This paper describes the combination of terrestrial vegetation observations from two sensors, providing a historical dataset used for an in-depth analysis of the corresponding spatio-temporal patterns. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is an important variable suitable for regional to large-scale [...] Read more.
This paper describes the combination of terrestrial vegetation observations from two sensors, providing a historical dataset used for an in-depth analysis of the corresponding spatio-temporal patterns. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is an important variable suitable for regional to large-scale monitoring of climate impacts on vegetation. In this work, we create an extensive dataset of FAPAR using a 10-day product at ∼1 km resolution from September, 1997, to April, 2012, combining information from two sensors: the NASA/Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the European Space Agency (ESA)/Medium Resolution Imaging Spectrometer Instrument (MERIS). The proposed methodology reduces the noise, fills the gaps and corrects for the spurious trends in the data, providing a time-consistent coverage of FAPAR. We develop a fast merging method and evaluate its performance over Europe and the Horn of Africa. Full article
(This article belongs to the Special Issue High Performance Computing in Remote Sensing)
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2637 KiB  
Article
Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique
by Gang Fu, Hongrui Zhao, Cong Li and Limei Shi
Remote Sens. 2013, 5(7), 3259-3279; https://doi.org/10.3390/rs5073259 - 5 Jul 2013
Cited by 36 | Viewed by 10772
Abstract
An approach based on the improved quadtree structure and region adjacency graph for the segmentation of a high-resolution remote sensing image is proposed in this paper. In order to obtain the initial segmentation results of the image, the image is first iteratively split [...] Read more.
An approach based on the improved quadtree structure and region adjacency graph for the segmentation of a high-resolution remote sensing image is proposed in this paper. In order to obtain the initial segmentation results of the image, the image is first iteratively split into quarter sections and the quadtree structure is constructed. In this process, an improved fast calculation method for standard deviation of image is proposed, which significantly increases the speed of quadtree segmentation with standard deviation criterion. A spatial indexing structure was built using improved Morton encoding based on this structure, which provides the merging process with data structure for neighborhood queries. Then, in order to obtain the final segmentation result, we constructed a feature vector using both spectral and texture factors, and proposed an algorithm for region merging based on the region adjacency graph technique. Finally, to validate the method, experiments were performed on GeoEye-1 and IKONOS color images, and the segmentation results were compared with two typical algorithms: multi-resolution segmentation and Mean-Shift segmentation. The experimental results showed that: (1) Compared with multi-resolution and Mean-Shift segmentation, our method increased efficiency by 3–5 times and 10 times, respectively; (2) Compared with the typical algorithms, the new method significantly improved the accuracy of segmentation. Full article
(This article belongs to the Special Issue High Performance Computing in Remote Sensing)
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1176 KiB  
Article
The Global Land Surface Satellite (GLASS) Remote Sensing Data Processing System and Products
by Xiang Zhao, Shunlin Liang, Suhong Liu, Wenping Yuan, Zhiqiang Xiao, Qiang Liu, Jie Cheng, Xiaotong Zhang, Hairong Tang, Xin Zhang, Qiang Liu, Gongqi Zhou, Shuai Xu and Kai Yu
Remote Sens. 2013, 5(5), 2436-2450; https://doi.org/10.3390/rs5052436 - 15 May 2013
Cited by 75 | Viewed by 14430
Abstract
Using remotely sensed satellite products is the most efficient way to monitor global land, water, and forest resource changes, which are believed to be the main factors for understanding global climate change and its impacts. A reliable remotely sensed product should be retrieved [...] Read more.
Using remotely sensed satellite products is the most efficient way to monitor global land, water, and forest resource changes, which are believed to be the main factors for understanding global climate change and its impacts. A reliable remotely sensed product should be retrieved quantitatively through models or statistical methods. However, producing global products requires a complex computing system and massive volumes of multi-sensor and multi-temporal remotely sensed data. This manuscript describes the ground Global LAnd Surface Satellite (GLASS) product generation system that can be used to generate long-sequence time series of global land surface data products based on various remotely sensed data. To ensure stabilization and efficiency in running the system, we used the methods of task management, parallelization, and multi I/O channels. An array of GLASS remote sensing products related to global land surface parameters are currently being produced and distributed by the Center for Global Change Data Processing and Analysis at Beijing Normal University in Beijing, China. These products include Leaf Area Index (LAI), land surface albedo, and broadband emissivity (BBE) from the years 1981 to 2010, downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) from the years 2008 to 2010. Full article
(This article belongs to the Special Issue High Performance Computing in Remote Sensing)
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