Name
Title | ||
|---|---|---|
A Target Detection Method Based on Low-Rank Regularized Least Squares Model for Hyperspectral Images. |
Abstract | ||
|---|---|---|
Target detection plays an important role in the field of hyperspectral image (HSI) remote sensing. In this letter, a novel matched subspace detector based on low-rank regularized least squares (LRLS-MSD) is proposed for hyperspectral target detection. As pixels in an HSI have global correlation and can be represented in subspace, the low-rank regularization is introduced in the least squares model... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LGRS.2016.2572090 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Object detection,Detectors,Hyperspectral imaging,Correlation,Wireless sensor networks,Adaptation models | Least squares,Computer vision,Object detection,Likelihood-ratio test,Subspace topology,Pattern recognition,Hyperspectral imaging,Regularization (mathematics),Pixel,Artificial intelligence,Detector,Mathematics | Journal |
Volume | Issue | ISSN |
13 | 8 | 1545-598X |
Citations | PageRank | References |
1 | 0.35 | 13 |
Authors | ||
5 | ||