Semi-supervised hyperspectral image segmentation

By Jun Yu Li, José M. Bioucas-Dias, Antonio J. Plaza. 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009

315 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/7d9b5afb5f793f57371201f8ae1b8e7efa17c377

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. Jocelyn Chanussot
3. Jun Yu Li
4. Jon Atli Benediktsson
5. José M. Bioucas-Dias
6. Danfeng Hong
7. Prashanth Reddy Marpu
8. Swalpa Kumar Roy

In Biomedical Engineering

1. Antonio J. Plaza
2. Jun Yu Li
3. Jocelyn Chanussot
4. Danfeng Hong
5. Jon Atli Benediktsson
6. José M. Bioucas-Dias
7. Swalpa Kumar Roy
8. L. Zhang

In Media Technology

1. Antonio J. Plaza
2. Jocelyn Chanussot
3. Jun Yu Li
4. José M. Bioucas-Dias
5. Jon Atli Benediktsson
6. Shutao Li
7. Danfeng Hong
8. Swalpa Kumar Roy

Citations in the corpus

• Generalized Composite Kernel Framework for Hyperspectral Image Classification (2013, methodology): “LORSAL plays a central role in this work, as in previous contributions [10], [11].”
• Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification (2019, methodology): “In the experiments, we use the default parameters for the compared methods (3-D discrete wavelet transform (3-D-DWT) [17], semisupervised HSI segmetation using multinomial logistic regression (semiMLR) [18], LapSVM [6], S2SL [19], SS-LapSVM [7], and spectral-spatial graph embedding learning (SSGEL) ”
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “TABLE III: Classification results obtained by RF [75], MLR [321], SVM [322], MLP [69], RNN [70], LSTM [323], GRU [324], CNN-1D [218], CNN-2D [325], CNN-3D [326], HybridSN [213], and MorphCNN [196] on the disjoint train-test dataset for the PU scene.”
• A Three-Layered Graph-Based Learning Approach for Remote Sensing Image Retrieval (2016, background): “W ITH the availability of huge volumes of high-resolution remote sensing images produced by all sorts of satellites and airborne sensors, remote sensing image processing and analysis have been an active research topic in the geoscience field [1]–[4] in recent decades.”
• Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network (2019, methodology): “Early spectral-based attempts including support vector machines (SVM) [9], multinomial logistic regression (MLR) [10,11], and random or dynamic subspace [12,13], focus on the spectral characteristics of HSI.”