Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines

By Lin He, Jun Yu Li, Chenying Liu, Shutao Li. IEEE Transactions on Geoscience and Remote Sensing, 2018

569 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/a80b169a86da15040d41033368a07ab052f4315d

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. Pedram Ghamisi
2. Antonio J. Plaza
3. Shutao Li
4. Javier Plaza
5. Jon Atli Benediktsson
6. Yushi Chen
7. Liangpei Zhang
8. Leyuan Fang

In Biomedical Engineering

1. Shutao Li
2. Pedram Ghamisi
3. Leyuan Fang
4. Jon Atli Benediktsson
5. Lei Guo
6. Antonio J. Plaza
7. Renlong Hang
8. Juan Mario Haut

In Media Technology

1. Antonio J. Plaza
2. Liangpei Zhang
3. Shutao Li
4. Pedram Ghamisi
5. Leyuan Fang
6. Gong Cheng
7. Junwei Han
8. Jon Atli Benediktsson

Citations in the corpus

• Deep Learning for Hyperspectral Image Classification: An Overview (2019, background): “data and increasing the classification accuracies [17], [18].”
• HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers (2020, background): “Despite the high spectral resolution, HSIs are originally 3-D and pictorial and have a spatial dependence, which is complementary to spectral behavior [16].”
• Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification (2019, background): “[32] resort to discriminative low-rank Gabor filtering which is shown to be particularly effective for spatial–spectral HSI classification.”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “13 Recent advances on spectral–spatial hyperspectral image classification: An overview and new guidelines [85] 2017 IEEE TGRS Survey of the progress in the classification of spectral–spatial hyperspectral images”
• Cascaded Recurrent Neural Networks for Hyperspectral Image Classification (2017, background): “How to combine them together has been an active research topic in the field of HSI classification [36], [37].”
• A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification (2021, background): “HSIs are composed of narrow and contiguous spectral bands in the electromagnetic spectrum [1]–[3].”