Abstract
Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information. They have made a plethora of applications feasible for the analysis of large areas of the Earth?s surface. However, a significant number of factors-such as the high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light-scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions-make such data inherently nonlinear and complex, which poses major challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal processing and have become a center of attention for researchers worldwide.
| Original language | English |
|---|---|
| Pages (from-to) | 37-78 |
| Number of pages | 42 |
| Journal | IEEE Geoscience and Remote Sensing Magazine |
| Volume | 5 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 31 Dec 2017 |
Keywords
- hyperspectral Imaging
- signal processing
- feature extraction
- image restoration
- spatial resolution
- algorithm design and analysis
- data preprocessing
- data analysis
- geophysical image processing
- remote sensing