Yaoming Cai

RoES: Rotational Equivariant Selective-frequency Fusion for Multimodal Images

Jiabao Wang, Wenjian Liu, Yaoming Cai, G. Zhang et autres

Infrared-visible image fusion facilitates robust multimodal perception by integrating complementary textural nuances from visible sensors with thermal signatures from infrared systems. Due to the task's inherently ill-posed nature, existing methods heavily rely on structural priors but typically enforce rotation equivariance uniformly across …

Beyond Implicit Constraint: Explicit Low-Rank Structured Subspace Learning for Fast Attributed Graph Clustering

Yaoming Cai, Song Liu, Zijia Zhang, You Wu et autres

Attributed graph clustering has achieved remarkable success by synergistically integrating topological structures and node attributes. While subspace learning has emerged as a dominant paradigm for node partitioning, most existing methods rely on implicit low-rank constraints, which often fail to capture complex nonlinear …

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High resolution indoor-outdoor urban PM2.5 daily dynamics with low cost solution using novel two-stage model of machine and transfer learning

Changhong Ou, Zhen Wang, Zhuokai Lu, Chang Gao et autres

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Uncertainty-Aware Deep Anchor Graph Learning for Multimodal Remote Sensing Image Clustering

Xiaodi Yu, Yaoming Cai, Z. Zhang, Yao Ding et autres

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Anchor-Guided Scalable Deep Subspace Clustering

Jiale Li, Yaoming Cai, Z. Zhang, Yao Ding et autres

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RFHA-YOLO: Dynamic Receptive Field and Adaptive Hybrid Attention for Small-Object Detection in Remote Sensing Images

Xiaobo Liu, Yiting Zheng, Yaoming Cai, Yao Ding et autres

Small object detection in remote sensing images remains challenging due to limited feature resolution and complex backgrounds. Conventional detectors, due to fixed receptive fields and uniform attention, struggle to capture small-target features and suffer from background clutter. To address these limitations, we …

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DSGC: Dynamic Sparse Graph Constrained Autoencoder for Hyperspectral Image Clustering

Feng Liang, Yao Ding, Zhili Zhang, Yaoming Cai et autres

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HSSTN: A Hybrid Spectral–Structural Transformer Network for High-Fidelity Pansharpening

Weijie Kang, Yuan Feng, Yao Ding, Hongbo Xiang et autres

Pansharpening fuses multispectral (MS) and panchromatic (PAN) remote sensing images to generate outputs with high spatial resolution and spectral fidelity. Nevertheless, conventional methods relying primarily on convolutional neural networks or unimodal fusion strategies frequently fail to bridge the sensor modality gap between …

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A robust low-pass filtering graph diffusion clustering framework for hyperspectral images

Aitao Yang, Min Li, Yao Ding, Yaoming Cai et autres

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SLCGC: A lightweight Self-supervised Low-pass Contrastive Graph Clustering Network for Hyperspectral Images

Yao Ding, Zhili Zhang, Aitao Yang, Yaoming Cai et autres

Self-supervised hyperspectral image (HSI) clustering remains a fundamental yet challenging task due to the absence of labeled data and the inherent complexity of spatial-spectral interactions. While recent advancements have explored innovative approaches, existing methods face critical limitations in clustering accuracy, feature discriminability, …

A cascaded autoencoder unmixing network for Hyperspectral anomaly detection

Kun Li, Yingqian Wang, Qiang Ling, Yaoming Cai et autres

Hyperspectral anomaly detection (HAD) is challenging especially when anomalies are presented in sub-pixel form.The spectral signatures of anomalies in mixed pixels are mixed with those of background, making anomalies difficult to be distinguished from background. Most existing methods detect sub-pixel targets in …

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MMAGL: Multiobjective Multiview Attributed Graph Learning for Joint Clustering of Hyperspectral and LiDAR Data

Zijia Zhang, Yaoming Cai, Wenyin Gong, Xiaobo Liu et autres

The joint clustering of multimodal remote sensing (RS) data represents a multiobjective optimization challenge involving conflicting modality-specific objectives and diverse regularization objectives. Current approaches to multiview subspace clustering (MVSC) often oversimplify this task by transforming it into a weighted single-objective optimization problem, …

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