Name
Title | ||
|---|---|---|
Canonical Correlation Feature Selection for Sensors With Overlapping Bands: Theory and Application |
Abstract | ||
|---|---|---|
The main focus of this paper is a rigorous development and validation of a novel canonical correlation feature- selection (CCFS) algorithm that is particularly well suited for spectral sensors with overlapping and noisy bands. The proposed approach combines a generalized canonical correlation analysis framework and a minimum mean-square-error criterion for the selection of feature subspaces. The latter induces ranking of the best linear combinations of the noisy overlapping bands and, in doing so, guarantees a minimal generalized distance between the centers of classes and their respective reconstructions in the space spanned by sensor bands. To demonstrate the efficacy and the scope of the proposed approach, two different applications are considered. The first one is separability and classification analysis of rock species using laboratory spectral data and a quantum-dot infrared photodetector (QDIP) sensor. The second application deals with supervised classification and spectral unmixing, and abundance estimation of hyperspectral imagery obtained from the Airborne Hyperspectral Imager sensor. Since QDIP bands exhibit significant spectral overlap, the first study validates the new algorithm in this important application context. The results demonstrate that proper postprocessing can facilitate the emergence of QDIP-based sensors as a promising technology for midwave- and longwave-infrared remote sensing and spectral imaging. In particular, the proposed CCFS algorithm makes it possible to exploit the unique advantage offered by QDIPs with a dot-in-a-well configuration, comprising their bias-dependent spectral response, which is attributable to the quantum Stark effect. The main objective of the second study is to assert that the scope of the new CCFS approach also extends to more traditional spectral sensors. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TGRS.2008.921637 | Geoscience and Remote Sensing, IEEE Transactions |
Keywords | Field | DocType |
feature extraction,geophysical techniques,image classification,rocks,Airborne Hyperspectral Imager sensor,CCFS algorithm,QDIP sensor,canonical correlation feature selection algorithm,feature subspaces selection,generalized canonical correlation analysis framework,longwave-infrared remote sensing,midwave-infrared remote sensing,minimum mean-square-error criterion,noisy overlapping bands,quantum Stark effect,quantum-dot infrared photodetector sensor,rock species,spectral imaging,spectral unmixing,supervised classification,Canonical correlation (CC) analysis,classification,dot-in-a-well (DWELL),feature selection,infrared photodetectors,quantum dots,spectral imaging,spectral sensing,subspace projection | Algorithm design,Spectral imaging,Feature selection,Canonical correlation,Remote sensing,Feature extraction,Hyperspectral imaging,Contextual image classification,Mathematics,Generalized canonical correlation | Journal |
Volume | Issue | ISSN |
46 | 10 | 0196-2892 |
Citations | PageRank | References |
6 | 0.64 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Biliana S. Paskaleva | 1 | 9 | 2.02 |
| Majeed M. Hayat | 2 | 213 | 26.36 |
| Zhipeng Wang | 3 | 52 | 17.96 |
| J. Scott Tyo | 4 | 9 | 3.43 |
| Sanjay Krishna | 5 | 11 | 3.62 |