التفاصيل البيبلوغرافية
| العنوان: |
Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques. |
| المؤلفون: |
Marpu, PrashanthReddy, Pedergnana, Mattia, Mura, MauroDalla, Peeters, Stijn, Benediktsson, JonAtli, Bruzzone, Lorenzo |
| المصدر: |
International Journal of Image & Data Fusion; Sep2012, Vol. 3 Issue 3, p269-298, 30p, 1 Color Photograph, 2 Diagrams, 6 Charts, 13 Maps |
| مصطلحات موضوعية: |
HYPERSPECTRAL imaging systems, MULTISENSOR data fusion, FEATURE extraction, PRINCIPAL components analysis, ALGORITHMS, DATA mining |
| مستخلص: |
The classification of remote sensing data based on the exploitation of spatial features extracted with morphological and attribute profiles has been recently gaining importance. With the development of efficient algorithms to construct the profiles for large datasets, such methods are becoming even more relevant. When dealing with hyperspectral imagery, the profiles are traditionally built on the first few principal components computed from the data. However, it needs to be determined if other feature reduction approaches are better suited to create base images for the profiles. In this article, we explore the use of profiles based on features derived from three supervised feature extraction techniques (i.e. Discriminant Analysis Feature Extraction, Decision Boundary Feature Extraction and Non-parametric Weighted Feature Extraction) and two unsupervised feature-extraction techniques (i.e. Principal Component Analysis (PCA) and Kernel PCA) in classification and compare the classification accuracies obtained by using different techniques for two different classification methods. The obtained results indicate significant improvements in the accuracies using the supervised feature extraction methods. However, the choice of the method affects the quality of the results for different datasets depending on the availability of the training samples. [ABSTRACT FROM AUTHOR] |
|
Copyright of International Journal of Image & Data Fusion is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
