CNN-based salient features in HSI image semantic target prediction
Deep networks have escalated the computational performance in the sensor-based high dimensional imaging such as hyperspectral images (HSI), due to their informative feature extraction competency. Therefore in this work, we have extracted the informative features from different CNN models for the ben...
Saved in:
| Main Authors: | Vishal Srivastava, Bhaskar Biswas |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-04-01
|
| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2019.1650330 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Dual-Branch Attention Convolution Spectral–Spatial Feature Extraction Networks for Hyperspectral Image Classification
by: Genyun Sun, et al.
Published: (2025-01-01) -
Collaborative Superpixelwised PCA for Hyperspectral Image Classification
by: Chao Yao, et al.
Published: (2025-01-01) -
EDB-Net: Efficient Dual-Branch Convolutional Transformer Network for Hyperspectral Image Classification
by: Hufeng Guo, et al.
Published: (2025-01-01) -
HCAFNet: Hierarchical Cross-Modal Attention Fusion Network for HSI and LiDAR Joint Classification
by: Jiajia Bai, et al.
Published: (2025-01-01) -
Central Pixel-Based Dual-Branch Network for Hyperspectral Image Classification
by: Dandan Ma, et al.
Published: (2025-04-01)