Name
Title | ||
|---|---|---|
A Novel Strategy of Nonnegative-Matrix-Factorization-Based Polarimetric Ship Detection |
Abstract | ||
|---|---|---|
In this letter, a new strategy based on nonnegative matrix factorization (NMF) is proposed for polarimetric ship detection. This method utilizes the sparse feature of nonnegative eigenvalues, and the sparse degree is proposed to be estimated from the histogram which can reveal the sparse distribution of eigenvalues. Combining the nonnegative and sparse features, the NMF-based ship detection method can be implemented flexibly and efficiently. It has been carried out on the C-band quad polarimetric synthetic aperture radar (PolSAR) and dual PolSAR ocean data sets to validate its effectiveness. Unlike a constant-false-alarm-rate detector, the NMF method does not depend on target size and therefore offers improved detection performance under low-signal-to-clutter-ratio conditions. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/LGRS.2011.2157077 | IEEE Geosci. Remote Sensing Lett. |
Keywords | Field | DocType |
nonnegative matrix factorization (nmf),synthetic aperture radar,eigenvalues,low-signal-to-clutter-ratio conditions,sparse distribution,ships,nonnegative eigenvalues,c-band quad polarimetric synthetic aperture radar,constant-false-alarm-rate detector,sparse degree,radar detection,matrix decomposition,target size,dual polsar ocean data sets,eigenvalues and eigenfunctions,nonnegative-matrix-factorization-based polarimetric ship detection,polarimetric ship detection,radar polarimetry,thyristors,nonnegative matrix factorization,constant false alarm rate,covariance matrix,clutter,detectors | Histogram,Pattern recognition,Synthetic aperture radar,Clutter,Matrix decomposition,Remote sensing,Artificial intelligence,Non-negative matrix factorization,Covariance matrix,Detector,Eigenvalues and eigenvectors,Mathematics | Journal |
Volume | Issue | ISSN |
8 | 6 | 1545-598X |
Citations | PageRank | References |
4 | 0.58 | 4 |
Authors | ||
6 | ||