Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox

By Behnood Rasti, Danfeng Hong, Renlong Hang, Pedram Ghamisi, Xudong Kang, Jocelyn Chanussot, Jon Atli Benediktsson. IEEE Geoscience and Remote Sensing Magazine, 2020

480 citations recorded by the source corpus.

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.

Subfields: Analytical Chemistry, Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/3d852a8aea396f2c110d4aeaec301030de93a8f8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Antonio J. Plaza
2. Yushi Chen
3. Qian Du
4. Jocelyn Chanussot
5. Wei Li
6. Danfeng Hong
7. Rubén Fernández-Beltran
8. Matteo Matteucci

In Artificial Intelligence

1. Danfeng Hong
2. Jocelyn Chanussot
3. Antonio J. Plaza
4. Qian Du
5. Jing Yao
6. Swalpa Kumar Roy
7. Bing Zhang
8. Wei Li

In Media Technology

1. Danfeng Hong
2. Jocelyn Chanussot
3. Antonio J. Plaza
4. Qian Du
5. Swalpa Kumar Roy
6. Jing Yao
7. Yushi Chen
8. Bing Zhang

Citations in the corpus

• Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, methodology): “The work in [66] deeply discusses the differences in techniques and tools to implement the feature extraction of HSI.”
• Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey (2021, background): “, DL) techniques [13]–[15], hierarchical feature representations with enough sample data can be learned with deep and complex networks.”
• Spatial-Spectral Transformer for Hyperspectral Image Classification (2021, background): “HSI classification aims to categorize the content of each pixel in the scene [9], which is a basic procedure in applications such as identifying the type of land-cover classes in earth monitoring [10].”
• SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers (2021, background): “random forest (RF), 1-D CNN [14], 2-D CNN [17], RNN [18], miniGCN [21], transformers [22], and the proposed SpectralFormer.”
• Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, background): “, 2013) Introducing basic concepts and features of HS and its relevant topics (Rasti et al., 2020) Providing a review of feature extraction approaches in HS (Moreira et al.”
• Attention-Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification (2020, unknown): “In the last decade, deep learning-based methods have proved very effective [14]–[16].”
• Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast (2020, background): “applications [1], [2], such as scene classification [3]–[7], object localization [8]–[12], and change detection [13]–[15], due to their prominent capabilities to uncover highly representative features from RS scenes [16].”
• Multimodal Fusion Transformer for Remote Sensing Image Classification (2022, unknown): “, KNN [58], RF [1], and SVM [27], as well as classical CNN models i.”
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, background): “the volume of a hypercube and hypersphere concentrates on corners and outside shells respectively [2].”