- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Local Adaptive Joint Sparse Representation for Hyperspectral Image Classification
摘要： In this paper, a local adaptive joint sparse representation (LAJSR) model is proposed for the classification of hyperspectral remote sensing images. It improves the original joint sparse representation (JSR) method in both the signal and dictionary construction phase and sparse representation phase. Given a testing pixel, a similar signal set is constructed by picking a few of the most similar pixels from its spatial neighborhood. The original training dictionary consists of training samples from different classes and is extended by adding spatial neighbors of each training sample. A local adaptive dictionary is built by selecting the most representative atoms from the extended dictionary that are correlated to the similar signal set. In the LAJSR framework, the selected similar signals are simultaneously represented by the local adaptive dictionary, and the obtained sparse representation coefficients are further weighted by a sparsity concentration index vector which aims to concentrate and highlight the coefficients on the expected class. Experimental results on two benchmark hyperspectral datasets have demonstrated that the proposed LAJSR method is much more effective than existing JSR and SVM methods, especially in the case of small sample sizes.
关键词： local adaptive dictionary,hyperspectral image,Classification,joint sparse representation
更新于2025-09-23 15:22:29
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[IEEE 2019 15th International Conference on Emerging Technologies (ICET) - Peshawar, Pakistan (2019.12.2-2019.12.3)] 2019 15th International Conference on Emerging Technologies (ICET) - Integrated Fault-Diagnoses and Fault-Tolerant MPPT Control Scheme for a Photovoltaic System
摘要： Visual tracking using multiple features has been proved as a robust approach because features could complement each other. Since different types of variations such as illumination, occlusion, and pose may occur in a video sequence, especially long sequence videos, how to properly select and fuse appropriate features has become one of the key problems in this approach. To address this issue, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. In order to capture the non-linear similarity of features, we extend the proposed method into a general kernelized framework, which is able to perform feature fusion on various kernel spaces. As a result, robust tracking performance is obtained. Both the qualitative and quantitative experimental results on publicly available videos show that the proposed method outperforms both sparse representation-based and fusion based-trackers.
关键词： joint sparse representation,feature fusion,Visual tracking
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Chaotic Communications in the Coupled Fiber Optic System
摘要： Visual tracking using multiple features has been proved as a robust approach because features could complement each other. Since different types of variations such as illumination, occlusion, and pose may occur in a video sequence, especially long sequence videos, how to properly select and fuse appropriate features has become one of the key problems in this approach. To address this issue, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. In order to capture the non-linear similarity of features, we extend the proposed method into a general kernelized framework, which is able to perform feature fusion on various kernel spaces. As a result, robust tracking performance is obtained. Both the qualitative and quantitative experimental results on publicly available videos show that the proposed method outperforms both sparse representation-based and fusion based-trackers.
关键词： feature fusion,joint sparse representation,Visual tracking
更新于2025-09-23 15:19:57
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[ACM Press the 2018 International Conference - Prague, Czech Republic (2018.10.12-2018.10.14)] Proceedings of the 2018 International Conference on Sensors, Signal and Image Processing - SSIP 2018 - SAR Target Recognition Based on Joint Sparse Representation of Complementary Features
摘要： This paper proposed a Synthetic Aperture Radar (SAR) target recognition method based on joint sparse representation of three complementary features. The Elliptical Fourier descriptors (EFDs) of the target outline and PCA features were extracted to depict the geometrical shape and intensity distribution of original SAR image. The azimuthal sensitivity image was constructed to describe the electromagnetic scattering characteristics of the target. The joint sparse representation was used to jointly classify the three features to exploit their complementary advantages. Finally, the target label of the test sample was decided based on the reconstruction errors. To validate the effeteness of the proposed method, experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under various operating conditions.
关键词： joint sparse representation,target recognition,Synthetic Aperture Radar (SAR),complementary features
更新于2025-09-19 17:15:36
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A High-Selectivity D-Band Mixed-Mode Filter Based on the Coupled Overmode Cavities
摘要： Visual tracking using multiple features has been proved as a robust approach because features could complement each other. Since different types of variations such as illumination, occlusion, and pose may occur in a video sequence, especially long sequence videos, how to properly select and fuse appropriate features has become one of the key problems in this approach. To address this issue, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. In order to capture the non-linear similarity of features, we extend the proposed method into a general kernelized framework, which is able to perform feature fusion on various kernel spaces. As a result, robust tracking performance is obtained. Both the qualitative and quantitative experimental results on publicly available videos show that the proposed method outperforms both sparse representation-based and fusion based-trackers.
关键词： feature fusion,joint sparse representation,Visual tracking
更新于2025-09-19 17:13:59