Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction

By Liangpei Zhang, Lefei Zhang, Dacheng Tao, Xin Huang. IEEE Transactions on Geoscience and Remote Sensing, 2013

308 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/9b365a0198fcede7b8e6c74ec01155558fb1431b

How researchers see this paper

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

In Computational Mechanics

1. Bo Du
2. Liangpei Zhang
3. Dacheng Tao
4. Jon Atli Benediktsson
5. Qian Zhang
6. Lefei Zhang
7. Shutao Li
8. Xudong Kang

In Computer Vision and Pattern Recognition

1. Liangpei Zhang
2. Bo Du
3. Lefei Zhang
4. Shutao Li
5. Xudong Kang
6. Xin Huang
7. Dacheng Tao
8. Jon Atli Benediktsson

In Media Technology

1. Liangpei Zhang
2. Shutao Li
3. Bo Du
4. Xudong Kang
5. Lefei Zhang
6. Jon Atli Benediktsson
7. Xin Huang
8. Dacheng Tao

Citations in the corpus

• Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding (2015, methodology): “In the hyperspectral image classification task, we usually use the kappa coefficient instead of AA to couple with OA for performance evaluation [47].”
• Slow Feature Analysis for Change Detection in Multispectral Imagery (2014, background): “With the development of Earth observation technology, multitemporal remote sensing data covering the same scene are now available, and both the spatial and spectral resolutions are improved [2]–[4].”
• Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network (2017, unknown): “Again, DR techniques may be utilized to extract low dimensional spectral–spatial features while preserving the discriminative information, such as tensor discriminative locality alignment (TDLA)-based feature extraction [26] and sparse low-rank approximation-based feature embedding [27].”
• Learning and Transferring Deep Joint Spectral–Spatial Features for Hyperspectral Classification (2017, background): “Most of the traditional hyperspectral classification methods rely on handcrafted features [4]–[15].”
• Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, background): “The HSI and MSI are cubic types of data [64] that contain two spatial dimensions (the width and height of channels) and one spectral dimension (number of channels).”
• Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016, unknown): “[82] L.”
• Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering (2014, background): “However, the high dimensionality of hyperspectral data may produce the Hughes phenomenon [1] and thus may influence the performance of supervised classification methods.”
• Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning (2014, unknown): “Recent work related to feature extraction from hyperspectral data has also addressed local spatial relationships via composite and other combined kernels [23], [44]–[46], tensor embedding [47], and iterative methods [22], [48,] [49].”