Hyperspectral Target Detection Using Gram-Schmidt Orthogonal Projections

5 Pages Posted: 8 Aug 2019

See all articles by Sherin Shibi C

Sherin Shibi C

Sri Venkateswara College of Engineering (SVCE) - Department of Electronics and Communication

Gayathri R

Sri Venkateswara College of Engineering (SVCE) - Department of Electronics and Communication

Date Written: August 7, 2019

Abstract

The automatic detection of targets in the hyperspectral images is highly used in defense and security applications. Hyperspectral images are rich in information due to its high spectral and spatial resolution. In this paper, we use Automatic Target Generation Process (ATGP) to extract the endmembers from the hyperspectral image. We compared the performance of Gram-Schmidt orthogonal vector projection (GSOVP) with the classical Orthogonal Subspace Projection (OSP). In our experiments, we used HYDICE and ROSIS urban datasets for evaluating the performance of the optimized algorithm.

Keywords: hyperspectral imaging, orthogonal projection, automatic target detection, Gram-Schmidt method

Suggested Citation

C, Sherin Shibi and R, Gayathri, Hyperspectral Target Detection Using Gram-Schmidt Orthogonal Projections (August 7, 2019). Proceedings of International Conference on Recent Trends in Computing, Communication & Networking Technologies (ICRTCCNT) 2019, Available at SSRN: https://ssrn.com/abstract=3433593 or http://dx.doi.org/10.2139/ssrn.3433593

Sherin Shibi C (Contact Author)

Sri Venkateswara College of Engineering (SVCE) - Department of Electronics and Communication ( email )

Chennai, Tamil Nadu 602117
India

Gayathri R

Sri Venkateswara College of Engineering (SVCE) - Department of Electronics and Communication ( email )

Chennai, Tamil Nadu 602117
India

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