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Huang Q.,George Mason University | Yang C.,George Mason University | Benedict K.,University of New Mexico | Chen S.,George Mason University | And 2 more authors.
International Journal of Digital Earth | Year: 2013

The simulations and potential forecasting of dust storms are of significant interest to public health and environment sciences. Dust storms have interannual variabilities and are typical disruptive events. The computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when dust storms come and scaling down when no dust storm events occur to save energy and costs. With the capability of providing a large, elastic, and virtualized pool of computational resources, cloud computing becomes a new and advantageous computing paradigm to resolve scientific problems traditionally requiring a large-scale and high-performance cluster. This paper examines the viability for cloud computing to support dust storm forecasting. Through a holistic study by systematically comparing cloud computing using Amazon EC2 to traditional high performance computing (HPC) cluster, we find that cloud computing is emerging as a credible solution for (1) supporting dust storm forecasting in spinning off a large group of computing resources in a few minutes to satisfy the disruptive computing requirements of dust storm forecasting, (2) performing high-resolution dust storm forecasting when required, (3) supporting concurrent computing requirements, (4) supporting real dust storm event forecasting for a large geographic domain by using recent dust storm event in Phoniex, 05 July 2011 as example, and (5) reducing cost by maintaining low computing support when there is no dust storm events while invoking a large amount of computing resource to perform high-resolution forecasting and responding to large amount of concurrent public accesses. © 2013 Copyright Taylor and Francis Group, LLC. Source


Wang G.,Institute of Remote Sensing Applications | Yan D.,Center for Earth Observation and Digital Earth | Yang Y.,Center for Earth Observation and Digital Earth
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

The dust on the camera's lens will leave dark stains on the image. Calibrating and compensating the intensity of the stained pixels play an important role in the airborne image processing. This article introduces an automatic compensation algorithm for the dark stains. It's based on the theory of flat-field correction. We produced a whiteboard reference image by aggregating hundreds of images recorded in one flight and use their average pixel values to simulate the uniform white light irradiation. Then we constructed a look-up table function based on this whiteboard image to calibrate the stained image. The experiment result shows that the proposed procedure can remove lens stains effectively and automatically. © 2010 Copyright SPIE - The International Society for Optical Engineering. Source


Tian X.,Center for Earth Observation and Digital Earth | Tian X.,University of Chinese Academy of Sciences | Wang C.,Center for Earth Observation and Digital Earth | Zhang H.,Center for Earth Observation and Digital Earth
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2011

Because of the influence of object orientation, sensor parameters and environmental factors, objects in SAR images have high changeability when the images were formed. The complex environment makes it difficult to extract object features we aim at. In this paper we take aircraft for example, propose a method to extract object features in high resolution SAR images. The features involve SURF features, numbers of engines, and the angle between engines and principal axes. Finally using the features combination we prove that the features extracted can effectively describe objects in various conditions. © 2011 IEEE. Source


Jiang S.,University of Chinese Academy of Sciences | Jiang S.,Center for Earth Observation and Digital Earth | Wang C.,Center for Earth Observation and Digital Earth | Zhang H.,Center for Earth Observation and Digital Earth | And 2 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

With the globalization there is an increasing degree of concern on the ship traffic monitoring. Civilian ship classification is an important research area, as it can help to improve sea traffic surveillance and control activities. By making use of the new generation SAR satellites like COSMO-SkyMed, civilian ship classification in high resolution SAR images is a hotspot and preceding problem in SAR applications. This paper presents a ship classification method that uses single-pol COSMO-SkyMed images to categorize civilian ships into three types, including bulk carriers, container ships and oil tankers. The experimental results based on ship structure features show that the whole classification accuracy is above 80%. © 2012 SPIE. Source


Nan J.,University of Chinese Academy of Sciences | Nan J.,Center for Earth Observation and Digital Earth | Zhang H.,Center for Earth Observation and Digital Earth | Wang C.,Center for Earth Observation and Digital Earth | And 3 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

This paper presents a novel normalized scanning algorithm for detecting ship wakes in SAR (Synthetic Aperture Radar) images. Unlike most of wake detection algorithm is based on Radon transform, the proposed algorithm is based on normalized scanning. The technique takes advantage of the displacement between the ship and perspective wake in azimuth direction. The proposed algorithm can determine the offset in azimuth direction and the movement direction of ship. Then we can get the velocity vector of the ship. Although the computational complexity is very small, the normalized scan algorithm is robust in high noise environment. Experiment work outs are carried over in real SAR images. Results show that the ship wake detection based on normalized scan is better than traditional technique. © 2012 SPIE. Source

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