Search Results for author: Bai Zhu

Found 6 papers, 0 papers with code

Exploiting Neighborhood Structural Features for Change Detection

no code implementations10 Feb 2023 Mengmeng Wang, Zhiqiang Han, Peizhen Yang, Bai Zhu, Ming Hao, Jianwei Fan, Yuanxin Ye

In this letter, a novel method for change detection is proposed using neighborhood structure correlation.

Change Detection

Advances and Challenges in Multimodal Remote Sensing Image Registration

no code implementations2 Feb 2023 Bai Zhu, Liang Zhou, Simiao Pu, Jianwei Fan, Yuanxin Ye

Over the past few decades, with the rapid development of global aerospace and aerial remote sensing technology, the types of sensors have evolved from the traditional monomodal sensors (e. g., optical sensors) to the new generation of multimodal sensors [e. g., multispectral, hyperspectral, light detection and ranging (LiDAR) and synthetic aperture radar (SAR) sensors].

Image Registration

R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor

no code implementations5 Dec 2022 Bai Zhu, Chao Yang, Jinkun Dai, Jianwei Fan, Yuanxin Ye

Automatically identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry.

Improving Co-registration for Sentinel-1 SAR and Sentinel-2 Optical images

no code implementations22 May 2020 Yuanxin Ye, Chao Yang, Bai Zhu, Youquan He, Huarong Jia

Finally, the obtained correspondences are employed to measure the misregistration shifts between the images.

Fast and Robust Registration of Aerial Images and LiDAR data Based on Structrual Features and 3D Phase Correlation

no code implementations21 Apr 2020 Bai Zhu, Yuanxin Ye, Chao Yang, Liang Zhou, Huiyu Liu, Yungang Cao

Subsequently, a robust structural feature descriptor is build based on dense gradient features, and the 3D phase correlation is used to detect control points (CPs) between aerial images and LiDAR data in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT).

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