1.7k citations · 3.9k across the 13 of their papers we have counts for
14 papers
ARSGaussian: 3D Gaussian Splatting with LiDAR for Aerial Remote Sensing Novel View Synthesis
Yiling Yao, Bing Zhang, Wenjuan Zhang +5
Novel View Synthesis (NVS) can reconstruct scenes from multi-view images and synthesize novel images from new viewpoints, which provides technical support for tasks such as target…
Hyperspectral Image Cross-Domain Object Detection Method based on Spectral-Spatial Feature Alignment
Hongqi Zhang, He Sun, Hongmin Gao +4
With consecutive bands in a wide range of wavelengths, hyperspectral images (HSI) have provided a unique tool for object detection task. However, existing HSI object detection meth…
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review
Minghua Wang, Danfeng Hong, Zhu Han +5
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the ob…
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review
Jiaxin Li, Danfeng Hong, Lianru Gao +4
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily…
SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers
Danfeng Hong, Zhu Han, Jing Yao +4
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepanci…
Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing
Danfeng Hong, Lianru Gao, Jing Yao +4
Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing, yet their ability to simultaneous…
Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing
Lianru Gao, Zhicheng Wang, Lina Zhuang +3
Tensor-based methods have been widely studied to attack inverse problems in hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented as a third-ord…
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations
Lina Zhuang, Lianru Gao, Bing Zhang +2
Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels.…
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Danfeng Hong, Wei He, Naoto Yokoya +5
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to proce…
Physically-Constrained Transfer Learning through Shared Abundance Space for Hyperspectral Image Classification
Ying Qu, Razieh Kaviani Baghbaderani, Wei Li +2
Hyperspectral image (HSI) classification is one of the most active research topics and has achieved promising results boosted by the recent development of deep learning. However, m…
More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
Danfeng Hong, Lianru Gao, Naoto Yokoya +4
Classification and identification of the materials lying over or beneath the Earth's surface have long been a fundamental but challenging research topic in geoscience and remote se…
Graph Convolutional Networks for Hyperspectral Image Classification
Danfeng Hong, Lianru Gao, Jing Yao +3
To read the final version please go to IEEE TGRS on IEEE Xplore. Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classifi…
Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution
Ke Zheng, Lianru Gao, Wenzhi Liao +4
Due to the limitations of hyperspectral imaging systems, hyperspectral imagery (HSI) often suffers from poor spatial resolution, thus hampering many applications of the imagery. Hy…
Spectral Superresolution of Multispectral Imagery with Joint Sparse and Low-Rank Learning
Lianru Gao, Danfeng Hong, Jing Yao +3
Extensive attention has been widely paid to enhance the spatial resolution of hyperspectral (HS) images with the aid of multispectral (MS) images in remote sensing. However, the ab…