Unsupervised mis-registration noise estimation in multi-temporal hyperspectral images

Salvatore Resta; Nicola Acito; Marco Diani; Giovanni Corsini

doi:10.1117/12.974216

8 November 2012 Unsupervised mis-registration noise estimation in multi-temporal hyperspectral images

Salvatore Resta, Nicola Acito, Marco Diani, Giovanni Corsini

Proceedings Volume 8537, Image and Signal Processing for Remote Sensing XVIII; 85370Q (2012) https://doi.org/10.1117/12.974216
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

Abstract

In this work, we focus on Anomalous Change Detection (ACD), whose goal is the detection of small changes occurred between two hyperspectral images (HSI) of the same scene. When data are collected by airborne platforms, perfect registration between images is very difficult to achieve, and therefore a residual mis-registration (RMR) error should be taken into account in developing ACD techniques. Recently, the Local Co-Registration Adjustment (LCRA) approach has been proposed to deal with the performance reduction due to the RMR, providing excellent performance in ACD tasks. In this paper, we propose a method to estimate the first and second order statistics of the RMR. The RMR is modeled as a unimodal bivariate random variable whose mean value and covariance matrix have to be estimated from the data. In order to estimate the RMR statistics, a feature description of each image is provided in terms of interest points extending the Scale Invariant Feature Transform (SIFT) algorithm to hyperspectral images, and false matches between descriptors belonging to different features are filtered by means of a highly robust estimator of multivariate location, based on the Minimum Covariance Determinant (MCD) algorithm. In order to assess the performance of the method, an experimental analysis has been carried out on a real hyperspectral dataset with high spatial resolution. The results highlighted the effectiveness of the proposed approach, providing reliable and very accurate estimation of the RMR statistics.

Citation Download Citation

Salvatore Resta, Nicola Acito, Marco Diani, and Giovanni Corsini "Unsupervised mis-registration noise estimation in multi-temporal hyperspectral images", Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85370Q (8 November 2012); https://doi.org/10.1117/12.974216

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Statistical analysis

Hyperspectral imaging

Sensors

Error analysis

Bismuth

Detection and tracking algorithms

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years