Feature Mining for Hyperspectral Image Classification

By Xiuping Jia, Bor-Chen Kuo, Melba M. Crawford. Proceedings of the IEEE, 2013

370 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/2b16fd0539c647f1ea77f0de7f42c49ea734daf4

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. Jon Atli Benediktsson
4. Shutao Li
5. Javier Plaza
6. Yushi Chen
7. Jocelyn Chanussot
8. Jun Yu Li

In Atmospheric Science

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

In Media Technology

1. Antonio J. Plaza
2. Shutao Li
3. Pedram Ghamisi
4. Jon Atli Benediktsson
5. Yushi Chen
6. Jocelyn Chanussot
7. Jun Yu Li
8. Danfeng Hong

Citations in the corpus

• Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “Relatively few bands can represent most of the information in HSIs [13], making DR very useful for storage, transmission, classification, spectral unmixing, target detection [14], and visualization of remote-sensing data [13], [15].”
• Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network (2015, neutral): “these methods: 1) feature selection (FS) and 2) feature extraction (FE) [13], [14].”
• Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016, background): “, in the aspects of handcrafted feature description [65]–[68], discriminative feature learning [13], [69], [70], and powerful classifier designing [71], [72].”
• Cascaded Recurrent Neural Networks for Hyperspectral Image Classification (2017, background): “Therefore, a large number of works were proposed to mine discriminative features from the high-dimensional spectral signature [5].”
• Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (2016, background): “However, due to the spatial variability of spectral signatures, HSI FE is still a challenging task [4].”
• Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework (2018, methodology): “Feature engineering methods include feature selection (band selection) and feature extraction [7].”
• Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, methodology): “The work of [15] reviewed feature extraction and data mining”
• Multi-scale 3D deep convolutional neural network for hyperspectral image classification (2017, background): “Recent researches show that, for the purpose of detection and classification of targets from HSI, the contextual information could provide great advantages, leading to a growth of interest in the research of joint spatial-spectral classification approaches [[1],[2]].”
• Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging (2016, background): “In addition, some classical data reduction techniques [1] are included for benchmarking.”
• Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, background): “Obviously, the feature extraction step, which incorporates the spatial information, is the key to the performance of these techniques [102].”