Matrix analysis
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
BACON: blocked adaptive computationally efficient outlier nominators
Computational Statistics & Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hyperspectral Data Exploitation: Theory and Applications
Hyperspectral Data Exploitation: Theory and Applications
Selection and Fusion of Color Models for Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binary sparse nonnegative matrix factorization
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Kernel-based regularized-angle spectral matching for target detection in hyperspectral imagery
Pattern Recognition Letters
The CFAR adaptive subspace detector is a scale-invariant GLRT
IEEE Transactions on Signal Processing
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
Efficient detection in hyperspectral imagery
IEEE Transactions on Image Processing
Unsupervised image classification, segmentation, and enhancement using ICA mixture models
IEEE Transactions on Image Processing
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
A Maximum Likelihood Approach to Joint Image Registration and Fusion
IEEE Transactions on Image Processing
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For hyperspectral target detection, it is usually the case that only part of the targets pixels can be used as target signatures, so can we use them to construct the most proper background subspace for detecting all the probable targets? In this paper, a dynamic subspace detection (DSD) method which establishes a multiple detection framework is proposed. In each detection procedure, blocks of pixels are calculated by the random selection and the succeeding detection performance distribution analysis. Manifold analysis is further used to eliminate the probable anomalous pixels and purify the subspace datasets, and the remaining pixels construct the subspace for each detection procedure. The final detection results are then enhanced by the fusion of target occurrence frequencies in all the detection procedures. Experiments with both synthetic and real hyperspectral images (HSI) evaluate the validation of our proposed DSD method by using several different state-of-the-art methods as the basic detectors. With several other single detectors and multiple detection methods as comparable methods, improved receiver operating characteristic curves and better separability between targets and backgrounds by the DSD methods are illustrated. The DSD methods also perform well with the covariance-based detectors, showing their efficiency in selecting covariance information for detection.