Object parameters optimization on pure and mixed pixels in thermal hyperspectral imagery

Xinyuan Miao; Ye Zhang; Junping Zhang; Shengwei Zhong

doi:10.1117/12.2272521

30 August 2017 Object parameters optimization on pure and mixed pixels in thermal hyperspectral imagery

Xinyuan Miao, Ye Zhang, Junping Zhang, Shengwei Zhong

Proceedings Volume 10403, Infrared Remote Sensing and Instrumentation XXV; 104030O (2017) https://doi.org/10.1117/12.2272521
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

The hyperspectral image in thermal infrared domains provide information, such as temperature and emissivity, about different kinds of materials. These information can be used for a wide number of applications such as mineral mapping, bathymetry, indoor and outdoor detection of chemicals. But because of the limitation of spatial resolution and the characteristics of thermal infrared sensor, there are many mixed pixels in the data, whose temperature，emissivity and abundance of different components can be hard to estimate. In this paper, a new method to estimate the parameters in pure and mixed pixels is proposed based on linear and nonlinear optimization. Firstly, the standard temperature and emissivity separation (TES) algorithm is applied on pure pixels of different materials selected by supervise or unsupervised methods to get the initial temperature. Secondly, the emissivity in different bands can be retrieved by minimizing the reconstruction error, which the more accurate temperature is optimized with. The emissivity in one band is trained by the samples in the same band but in different pixels, while the temperature is trained by different bands in one pixel. Lastly, the abundance and temperature of components in mixed pixels are estimated based on a linear mixture model of the bottom of atmosphere radiance as full constraint linear optimization problem and nonlinear optimization problem. The method is also analyzed with respect to sensitivity to the noise and different parameters’ influences on estimation errors.

Conference Presentation

Citation Download Citation

Xinyuan Miao, Ye Zhang, Junping Zhang, and Shengwei Zhong "Object parameters optimization on pure and mixed pixels in thermal hyperspectral imagery", Proc. SPIE 10403, Infrared Remote Sensing and Instrumentation XXV, 104030O (30 August 2017); https://doi.org/10.1117/12.2272521

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available