The application of the covariance matrix statistical method for removing atmospheric effects from satellite remotely sensed data intended for environmental applications

Diofantos G. Hadjimitsis; Chris Clayton

doi:10.1117/12.751887

30 October 2007 The application of the covariance matrix statistical method for removing atmospheric effects from satellite remotely sensed data intended for environmental applications

Diofantos G. Hadjimitsis, Chris Clayton

Author Affiliations +

Proceedings Volume 6749, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII; 674936 (2007) https://doi.org/10.1117/12.751887
Event: SPIE Remote Sensing, 2007, Florence, Italy

Abstract

The Covariance Matrix Method (CMM) uses the statistical relationship between all the selected bands of a satellite sensor simultaneously, rather than one at a time as in the regression method. It examines the set of variances and covariance between all band pairs in the image data and CMM provides an average pixel correction for a specified part of a satellite image. It is necessary to know a priori a value for the atmospheric path radiance on one spectral band. From this, CMM enables the estimation of the atmospheric path radiances in all the other bands. Dark pixels must be present in the CMM technique. Indeed, the authors suggest an improved CMM atmospheric correction algorithm. This methodology has been presented as an improved revised version of the CMM atmospheric approach. The authors provide a critical assessment of the suitability of the CMM atmospheric correction using Landsat TM image data of an area consisting low reflectance targets that have been used for several environmental monitoring applications. The proposed improved method produces retrieved surface reflectance within the range of the ground measurements.

Citation Download Citation

Diofantos G. Hadjimitsis and Chris Clayton "The application of the covariance matrix statistical method for removing atmospheric effects from satellite remotely sensed data intended for environmental applications", Proc. SPIE 6749, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VII, 674936 (30 October 2007); https://doi.org/10.1117/12.751887

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