Deep Few-Shot Learning for Hyperspectral Image Classification
By Bing Liu, Xuchu Yu, Anzhu Yu, Pengqiang Zhang, Gang Wan, Ruirui Wang. IEEE Transactions on Geoscience and Remote Sensing, 2019
395 citations recorded by the source corpus.
Deep learning methods have recently been successfully explored for hyperspectral image (HSI) classification. However, training a deep-learning classifier notoriously requires hundreds or thousands of labeled samples. In this paper, a deep few-shot learning method is proposed to address the small sample size problem of HSI classification. There are three novel strategies in the proposed algorithm. First, spectral–spatial features are extracted to reduce the labeling uncertainty via a deep residual 3-D convolutional neural network. Second, the network is trained by episodes to learn a metric space where samples from the same class are close and those from different classes are far. Finally, the testing samples are classified by a nearest neighbor classifier in the learned metric space. The key idea is that the designed network learns a metric space from the training data set. Furthermore, such metric space could generalize to the classes of the testing data set. Note that the classes of the testing data set are not seen in the training data set. Four widely used HSI data sets were used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can achieve better classification accuracy than the conventional semisupervised methods with only a few labeled samples.
Subfields: Artificial Intelligence, Atmospheric Science, Media Technology
View Paper: https://www.semanticscholar.org/paper/99ce3da843b5a1a0100b60d705e61e00892cfe42
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Qian Du
- Jocelyn Chanussot
- Wei Li
- Alberto Signoroni
- Annalisa Baronio
- Mattia Savardi
- Sergio Benini
- Ran Tao
In Atmospheric Science
- Qian Du
- Wei Li
- Mengmeng Zhang
- Jocelyn Chanussot
- Alberto Signoroni
- Xiao Xiang Zhu
- Annalisa Baronio
- Lichao Mou
In Media Technology
- Qian Du
- Wei Li
- Jocelyn Chanussot
- Mengmeng Zhang
- Alberto Signoroni
- Annalisa Baronio
- Mattia Savardi
- Sergio Benini
Citations in the corpus
- Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review (2019, background): “In [158], Liu et al. described a 3-”
- A review of deep learning used in the hyperspectral image analysis for agriculture (2021, methodology): “A novel tensorial approach, namely, generalized regression, is proposed for hyperspectral image classification (Liu et al. 2019).”
- Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification (2022, methodology): “Liu et al. [33] proposed deep FSL (DFSL) to classify HSI with few labeled data.”
- Self Supervised Learning for Few Shot Hyperspectral Image Classification (2022, methodology): “These paradigms have been utilized on hyperspectral data as well [1, 2, 3] showing promising results.”