Multi-scale 3D deep convolutional neural network for hyperspectral image classification

By Mingyi He, Bo Li, Huahui Chen. International Conference on Information Photonics, 2017

410 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/2e48e18e6bcab2f8132908e0d95467c014266223

How researchers see this paper

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

In Atmospheric Science

1. Yushi Chen
2. Jocelyn Chanussot
3. Swalpa Kumar Roy
4. Danfeng Hong
5. Zhouhan Lin
6. Bo Du
7. Lorenzo Bruzzone
8. Bertrand Le Saux

In Computer Vision and Pattern Recognition

1. Jocelyn Chanussot
2. Danfeng Hong
3. Swalpa Kumar Roy
4. Bo Du
5. Lefei Zhang
6. Yushi Chen
7. Bertrand Le Saux
8. Jing Yao

In Media Technology

1. Jocelyn Chanussot
2. Danfeng Hong
3. Swalpa Kumar Roy
4. Yushi Chen
5. Bo Du
6. Lorenzo Bruzzone
7. Jing Yao
8. Lefei Zhang

Citations in the corpus

• Attention-Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification (2020, methodology): “We compare classical machine learning and representative deep learning methods available in [63] 2 : random forest (RF) [64], multinomial logistic regression (MLR) [65], support vector machine (SVM) with radial basis function [1], gated recurrent unit (GRU) [66], long short term memory (LSTM) [67], ”
• Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification (2019, background): “To take the multi-scale cues into consideration, we construct multiple graphs with different neighborhood scales so that the spatial information at different scales can be fully exploited [32].”
• Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, positive): “Many architectures have been proposed to handle 3D CNNs for hyperspectral data, mostly to investigate well-known techniques from deep learning for computer vision, such as multiscale feature extraction [69] and semisupervision [70].”
• Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, positive): “In [156], a multiscale 3D deep CNN is introduced”
• HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification (2019, methodology): “The results of the proposed H ybrid SN model are compared with the most widely used supervised methods, such as SVM [33], 2-D-CNN [34], 3-D-CNN [35], multi-scale 3-D deep convolutional neural network (M3D)-CNN [36], and SSRN [27].”
• Spatial-Spectral Transformer for Hyperspectral Image Classification (2021, methodology): “For example, in [33], He et al. proposed a 3D deep CNN to jointly extracted spatial and spectral features by computing multiscale features.”