Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4970229 | Pattern Recognition Letters | 2016 | 15 Pages |
Abstract
This paper reviews the state-of-the-art representation-based classification and detection approaches for hyperspectral remote sensing imagery, including sparse representation-based classification (SRC), collaborative representation-based classification (CRC), and their extensions. In addition to the original SRC and CRC, the related techniques are categorized into the following subsections: (1) representation-based classification with dictionary partition using class-specific labeled samples; (2) representation-based classification with weighted regularization by measuring similarity between each atom and a testing sample; (3) representation-based classification with joint structured models to consider contextual information during recovery optimization; (4) representation using spatial features in a preprocessing or a postprocessing step; (5) representation-based classification in a high-dimensional kernel space through nonlinear mapping; and (6) target and anomaly detection with sparse and collaborative representations. Some open issues and ongoing investigations in this field are also discussed.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Wei Li, Qian Du,