By Topic

Experiments on feature extraction in remotely sensed hyperspectral image data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Zortea, M. ; Center for Remote Sensing, Fed. Univ., Rio Grande Do Sul, Brazil ; Haertel, V.

In the present study, we propose a new simple approach to reduce the data dimensionality in hyperspectral image data. The basic assumption here consists in assuming that a pixel's curve of spectral response, as defined in the spectral space by the recorded digital numbers (DNs) at the available spectral bands, can be segmented, and each segment can be replaced by a smaller number of statistics: mean and variance, describing the main characteristics of a pixel's spectral response. It is expected that this procedure can be accomplished without significant loss of information. The DNs at even spectral band are used to calculate a few statistics that would be used instead of the DNs themselves in the classification process. For the pixel's spectral curve segmentation, we propose tree sub-optimal algorithms that are easy to implement and also computationally efficient. Using a top-down strategy, the original pixel's spectral curve is sequentially segmented. Experiments using a parametric classifier are performed on an AVIRIS data set. Encouraging results have been obtained in terms of classification accuracy and execution time, suggesting the effectiveness of the proposed algorithms. The results suggest that the proposed algorithms can be faster and achieve a better accuracy than the classical Sequential Forward Selection (SFS) technique, known from literature as one of the simplest and fastest techniques for data dimensionality reduction using the feature selection approach.

Published in:

Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International  (Volume:2 )

Date of Conference:

20-24 Sept. 2004