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An Efficient Hardware Implementation of Detecting Targets from Remotely Sensed Hyperspectral Images

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Title An Efficient Hardware Implementation of Detecting Targets from Remotely Sensed Hyperspectral Images
 
Creator Shibi, C Sherin
Gayathri, R
 
Subject Automatic target generation process
Field programmable gate array
Gram-Schmidt orthogonalization
Hyperspectral imaging
Onboard processing
 
Description 156-165
Real-time implementation of hyperspectral imagery is an emerging research area which has notable remote sensing applications. It is challenging to process a huge volume of hyperspectral data under real-time constraints. Field programmable gate arrays are considered as an efficient hardware suited for onboard processing system. ATGP is a proven target detection algorithm which can automatically detect the target without any predefined data. In the traditional method, this algorithm involves orthogonal subspace projector which makes the hardware design too complex and slow. To speed up the process, Gram-Schmidt orthogonalization operator is used. Gram-Schmidt orthogonalization technique uses inner product instead of matrix inverse which makes the hardware design easy to implement in FPGA board. A detailed comparative analysis is carried out using three different hyperspectral images to emphasize the performance of the design which is adopted in this technique. The processing speed of the proposed ATGP-GS algorithm is 3.484 s for ROSIS Pavia University dataset, 1.781 s for HYDICE Urban dataset and 1.609 s for AVIRIS Cuprite dataset. The proposed algorithm is implemented in Virtex 6 ML605 evaluation board to evaluate the real-time performance of the system.
 
Date 2022-02-04T11:36:36Z
2022-02-04T11:36:36Z
2022-02
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/59083
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(02) [Feburary 2022]