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Your search keyword '"Wei, Zhihui"' showing total 26 results
Start Over Author "Wei, Zhihui" Journal ieee transactions on geoscience and remote sensing
26 results on '"Wei, Zhihui"'

1. Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences

Author

Xu, Yang, Wu, Zebin, Chanussot, Jocelyn, Dalla Mura, Mauro, Bertozzi, Andrea L, and Wei, Zhihui

Subjects
Detection, hyperspectral video sequences, low-rank, sparse, and total variation, low-rank, sparse, and total variation, Geological & Geomatics Engineering, Geophysics, Electrical and Electronic Engineering, Geomatic Engineering
Published
2018

6. Hyperspectral Images Single-Source Domain Generalization Based on Nonlinear Sample Generation

Author

Wang, Biqi, Xu, Yang, Wu, Zebin, Zheng, Shangdong, Wei, Zhihui, and Chanussot, Jocelyn

Abstract

In hyperspectral cross-scene classification tasks, it is often challenging to obtain target domain samples during the training phase. Therefore, models need to be trained on one or multiple source domains and achieve good generalization performance on unknown target domains, known as domain generalization. The presence of domain shift limits the model’s generalization across different domains, while the unknown target domain makes it difficult to accurately characterize the distribution differences between domains. To address this issue, we propose a generalization network based on nonlinear sample generation. The network divides the sample features into invariant features and variant features and generates samples by applying nonlinear transformations to the variant features. To ensure the quality of the generated samples, we introduce contrastive learning into the model. It ensures consistency in similarity between the generated samples and the source samples while maintaining a certain degree of dissimilarity. Experiments conducted on four cross-domain adaptive scenarios demonstrate the superior performance of our proposed method.

Published
2024
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7. Connecting Low-Level and High-Level Visions: A Joint Optimization for Hyperspectral Image Super-Resolution and Target Detection

Author

He, Chengxun, Xu, Yang, Wu, Zebin, and Wei, Zhihui

Abstract

Typical high-level vision tasks in hyperspectral image (HSI) processing, such as target detection, often suffer from insufficient information inherent in real-world sampled data. Super-resolution, a powerful tool in HSI low-level vision, is expected to enhance the accuracy of detection results by computationally providing the high-resolution HSI (HR-HSI) with additional information. However, existing solutions for HSI super-resolution and target detection have always been implemented independently. This conventionally adopted paradigm overlooks the interconnectedness between low-level and high-level visions, inevitably introducing additional errors, redundancies, and inefficiencies. To address this challenge, in this study, we put our efforts into exploring the uncharted continent of hyperspectral remote sensing, that is, realizing the mutual guidance and joint optimization of HSI super-resolution and target detection concurrently within a unified framework. Technically, we first construct different spectral bases to span the target and background subspaces of the underlying HR-HSI. Then, we look in-depth at the intrinsic properties of the HSI tensor, henceforth jointly optimizing both tasks by innovatively developing a novel low-cubic-rank tensor approximation model with a unique constrained energy minimization (CEM) loss. While we have developed efficient algorithms to optimize the proposed model, we also put into place a refinement procedure for spectral bases, aimed at further enhancing the spectral fidelity of the fused results and the compact representation of the target subspace. Finally, empirical studies conducted on synthetic and real-world datasets substantiate that compared with state-of-the-art solutions, the proposed method delivers highly competitive and practical performance in terms of both tasks. Source codes are available at https://github.com/CX-He/HySRTD.git.

Published
2024
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