Spatial aware probabilistic multi-kernel collaborative representation for hyperspectral image classification using few labelled samples
Author:
Affiliation:
1. Department of Electrical and Electronics Engineering, Bandırma Onyedi Eylul University, Balıkesir, Turkey
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences
Link
https://www.tandfonline.com/doi/pdf/10.1080/01431161.2020.1823516
Reference43 articles.
1. Semisupervised Hyperspectral Image Classification Using Deep Features
2. Intrinsic images in the wild
3. Kernel-based methods for hyperspectral image classification
4. Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection
5. Composite Kernels for Hyperspectral Image Classification
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2. Mean-Weighted Collaborative Representation-Based Spatial-Spectral Joint Classification for Hyperspectral Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
3. Advances in Hyperspectral Image Classification Methods with Small Samples: A Review;Remote Sensing;2023-07-30
4. Hyperspectral Image Classification Based on 3D-2D Hybrid Convolution and Graph Attention Mechanism;2023-07-18
5. Diversity-Driven Multikernel Collaborative Representation Ensemble for Hyperspectral Image Classification;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2022
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