An Unsupervised Momentum Contrastive Learning Based Transformer Network for Hyperspectral Target Detection
Author:
Affiliation:
1. Information Science and Technology College, Dalian Maritime University, Dalian, China
2. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Liaoning Province
China Postdoctoral Science Foundation
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/4609443/10330207/10497838.pdf?arnumber=10497838
Reference60 articles.
1. Hyperspectral Target Detection : An Overview of Current and Future Challenges
2. An Unsupervised Domain Adaptation Method Towards Multi-Level Features and Decision Boundaries for Cross-Scene Hyperspectral Image Classification
3. Distillation-Constrained Prototype Representation Network for Hyperspectral Image Incremental Classification
4. Self-Supervised Deep Multi-Level Representation Learning Fusion-Based Maximum Entropy Subspace Clustering for Hyperspectral Band Selection
5. Cross-Scene Hyperspectral Image Classification With Discriminative Cooperative Alignment
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1. DCANet: Dense Convolutional Attention Network for infrared small target detection;Measurement;2025-01
2. Tucker Decomposition-Based Network Compression for Anomaly Detection With Large-Scale Hyperspectral Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
3. Deep Spatial–Spectral Joint-Sparse Prior Encoding Network for Hyperspectral Target Detection;IEEE Transactions on Cybernetics;2024
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