Abstract:
Hyperspectral images (HSIs) provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands), which can be use...Show MoreMetadata
Abstract:
Hyperspectral images (HSIs) provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands), which can be used to accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to conventional techniques (the so-called curse of dimensionality) for accurate analysis of HSIs.
Published in: IEEE Geoscience and Remote Sensing Magazine ( Volume: 8, Issue: 4, December 2020)
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- IEEE Keywords
- Index Terms
- Image Processing ,
- Signal Processing ,
- Classification Accuracy ,
- Senior Researchers ,
- Curse Of Dimensionality ,
- Spectral Channels ,
- Hyperspectral Image Classification ,
- Feature Extraction Approach ,
- Convolutional Neural Network ,
- Spatial Information ,
- Hidden Layer ,
- Feature Space ,
- Linear Discriminant Analysis ,
- Spectral Bands ,
- Long Short-term Memory ,
- Recurrent Neural Network ,
- Low-dimensional Space ,
- Hyperspectral Data ,
- Group Technique ,
- Affinity Matrix ,
- Feature Extraction Techniques ,
- Stacked Autoencoder ,
- Locally Linear Embedding ,
- Kernel Principal Component Analysis ,
- Smooth Features ,
- Joint Learning ,
- Spectral Spatial Feature Extraction ,
- Low-dimensional Feature Space ,
- Spatial Features ,
- Spectral Features
Contents Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Image Processing ,
- Signal Processing ,
- Classification Accuracy ,
- Senior Researchers ,
- Curse Of Dimensionality ,
- Spectral Channels ,
- Hyperspectral Image Classification ,
- Feature Extraction Approach ,
- Convolutional Neural Network ,
- Spatial Information ,
- Hidden Layer ,
- Feature Space ,
- Linear Discriminant Analysis ,
- Spectral Bands ,
- Long Short-term Memory ,
- Recurrent Neural Network ,
- Low-dimensional Space ,
- Hyperspectral Data ,
- Group Technique ,
- Affinity Matrix ,
- Feature Extraction Techniques ,
- Stacked Autoencoder ,
- Locally Linear Embedding ,
- Kernel Principal Component Analysis ,
- Smooth Features ,
- Joint Learning ,
- Spectral Spatial Feature Extraction ,
- Low-dimensional Feature Space ,
- Spatial Features ,
- Spectral Features
