Search Results for author: Liang Zhou

Found 9 papers, 3 papers with code

Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection

1 code implementation10 Feb 2023 Yuanxin Ye, Mengmeng Wang, Liang Zhou, Guangyang Lei, Jianwei Fan, Yao Qin

First, through the inner fusion property of 3D convolution, we design a new feature fusion way that can simultaneously extract and fuse the feature information from bi-temporal images.

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

Implicit Multidimensional Projection of Local Subspaces

1 code implementation7 Sep 2020 Rongzheng Bian, Yumeng Xue, Liang Zhou, Jian Zhang, Baoquan Chen, Daniel Weiskopf, Yunhai Wang

We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation.

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).

Generalized Energy Based Models

1 code implementation ICLR 2021 Michael Arbel, Liang Zhou, Arthur Gretton

We show that both training stages are well-defined: the energy is learned by maximising a generalized likelihood, and the resulting energy-based loss provides informative gradients for learning the base.

Image Generation

Cost and Actual Causation

no code implementations31 Jul 2017 Liang Zhou

I propose the purpose our concept of actual causation serves is minimizing various cost in intervention practice.

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