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pro vyhledávání: '"Ran Tao"'
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-13
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:1037-1050
The key to hyperspectral anomaly detection is to effectively distinguish anomalies from the background, especially in the case that background is complex and anomalies are weak. Hyperspectral imagery (HSI) as an image-spectrum merging cube data can b
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-15
Due to the limitations of single-source data, joint classification using multisource remote sensing data has received increasing attention. However, existing methods still have certain shortcomings when faced with feature extraction from single-sourc
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-15
In this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two principles for spectral-spatial feature extraction of HSI are built, including the foundation of pixel-level HSI classification and the definition of spati
Publikováno v:
IEEE transactions on neural networks and learning systems.
With the recent development of the joint classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data, deep learning methods have achieved promising performance owing to their locally sematic feature extracting ability. No
Publikováno v:
IEEE transactions on neural networks and learning systems.
Most domain adaptation (DA) methods in cross-scene hyperspectral image classification focus on cases where source data (SD) and target data (TD) with the same classes are obtained by the same sensor. However, the classification performance is signifi
Publikováno v:
IEEE transactions on neural networks and learning systems.
Joint classification using multisource remote sensing data for Earth observation is promising but challenging. Due to the gap of imaging mechanism and imbalanced information between multisource data, integrating the complementary merits for interpret
Publikováno v:
IEEE transactions on neural networks and learning systems.
Domain adaptation techniques have been widely applied to the problem of cross-scene hyperspectral image (HSI) classification. Most existing methods use convolutional neural networks (CNNs) to extract statistical features from data and often neglect t