scholarly journals A Dynamic Remote Sensing Data-Driven Approach for Oil Spill Simulation in the Sea

2015 ◽  
Vol 7 (6) ◽  
pp. 7105-7125 ◽  
Author(s):  
Jining Yan ◽  
Lizhe Wang ◽  
Lajiao Chen ◽  
Lingjun Zhao ◽  
Bomin Huang
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
Remote Sensing Data ◽  
Data Driven ◽  
Sensing Data ◽  
Data Driven Approach

High performance GPU computing based approaches for oil spill detection from multi-temporal remote sensing data

2017 ◽  
Vol 202 ◽  
pp. 28-44 ◽  
Author(s):  
Ujwala Bhangale ◽  
Surya S. Durbha ◽  
Roger L. King ◽  
Nicolas H. Younan ◽  
Rangaraju Vatsavai
Keyword(s):  
Remote Sensing ◽  
Oil Spill ◽  
High Performance ◽  
Gpu Computing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Oil Spill Detection ◽  
Multi Temporal

scholarly journals A data-driven epidemiological prediction method for dengue outbreaks using local and remote sensing data

2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Anna L Buczak ◽  
Phillip T Koshute ◽  
Steven M Babin ◽  
Brian H Feighner ◽  
Sheryl H Lewis
Keyword(s):  
Remote Sensing ◽  
Prediction Method ◽  
Remote Sensing Data ◽  
Data Driven ◽  
Sensing Data ◽  
Dengue Outbreaks

Research on Parallel Algorithm of Sea Surface Oil Spill Identification Based on GPU

2013 ◽  
Vol 823 ◽  
pp. 631-635 ◽  
Author(s):  
Yan Zhou ◽  
Bin Zhou ◽  
Ying Ying Gai
Keyword(s):  
Remote Sensing ◽  
Parallel Algorithm ◽  
Oil Spill ◽  
Remote Sensing Data ◽  
Sea Surface ◽  
Process Unit ◽  
Image Size ◽  
Sensing Data ◽  
Speed Up ◽  
Oil Spill Identification

To overcome the drawback that spectral matching is time consuming because of the large volume of remote sensing data, a new parallel algorithm of sea surface oil spill identification based on Graphic Process Unit (GPU) is presented in this paper by taking advantage of HJ-1 CCD remote sensing data. Taking minimum distance classification as an example, we reorganized the pre-processed image data, selected reference spectral data and stored in constant memory, and then the algorithm core code of the host was transplanted to the device so as to realize parallelization on NVIDIA GeForce GTX 550Ti device. Results showed that, the maximum speed-up ratio is up to 102. Finally, the advantage of GPU parallel computing for computationally intensive problems has also been validated through experiments. When the image size is 2048x2048, speed-up ratio is up to 89.

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