Related references
Note: Only part of the references are listed.Weighted Collaborative Sparse and L1/2 Low-Rank Regularizations With Superpixel Segmentation for Hyperspectral Unmixing
Le Sun et al.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)
TSLRLN: Tensor subspace low-rank learning with non-local prior for hyperspectral image mixed denoising
Chengxun He et al.
SIGNAL PROCESSING (2021)
Hyperspectral image classification with unsupervised feature extraction
Qiaoqiao Sun et al.
REMOTE SENSING LETTERS (2020)
SLRL4D: Joint Restoration ofSubspaceLow-RankLearning and Non-Local4-DTransform Filtering for Hyperspectral Image
Le Sun et al.
REMOTE SENSING (2020)
Research on land use classification of hyperspectral images based on multiscale superpixels
Hua Wang et al.
MATHEMATICAL BIOSCIENCES AND ENGINEERING (2020)
Querying Representative and Informative Super-Pixels for Filament Segmentation in Bioimages
Wei Shao et al.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2020)
Automatic Tuning of the RBF Kernel Parameter for Batch-Mode Active Learning Algorithms: A Scalable Framework
Chin-Chun Chang et al.
IEEE TRANSACTIONS ON CYBERNETICS (2019)
A new deep convolutional neural network for fast hyperspectral image classification
M. E. Paoletti et al.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2018)
Performance analysis of support vector machine based classifiers
Zulfiqar Ali et al.
INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES (2018)
Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection
Gustavo Camps-Valls et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2008)
Share Paper
