J
Ji Zhao
Researcher at China University of Geosciences (Wuhan)
Publications - 61
Citations - 2312
Ji Zhao is an academic researcher from China University of Geosciences (Wuhan). The author has contributed to research in topics: Conditional random field & Hyperspectral imaging. The author has an hindex of 18, co-authored 52 publications receiving 1354 citations. Previous affiliations of Ji Zhao include Wuhan University.
Papers
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Locality Preserving Matching
TL;DR: The authors' method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds, and achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude.
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Guided Locality Preserving Feature Matching for Remote Sensing Image Registration
TL;DR: This paper proposes a simple yet surprisingly effective approach, termed as guided locality preserving matching, for robust feature matching of remote sensing images, and formulate it into a mathematical model, and derive a simple closed-form solution with linearithmic time and linear space complexities.
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LMR: Learning a Two-Class Classifier for Mismatch Removal
TL;DR: This paper casts the mismatch removal into a two-class classification problem, learning a general classifier to determine the correctness of an arbitrary putative match, termed as Learning for Mismatch Removal (LMR).
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WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF
TL;DR: In the proposed method, a deep convolutional neural network is designed to extract and fuse in-depth spectral and local spatial features, and the conditional random field (CRF) model further incorporates the spatial-contextual information to improve the problem of holes and isolated regions in the classification map.
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Nonrigid Point Set Registration With Robust Transformation Learning Under Manifold Regularization
TL;DR: This paper solves the problem of nonrigid point set registration by designing a robust transformation learning scheme and applies the proposed method to learning motion flows between image pairs of similar scenes for visual homing, which is a specific type of mobile robot navigation.