1.4k citations · 2.9k across the 13 of their papers we have counts for
22 papers
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction
Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot +2
Conventional nonlinear subspace learning techniques (e.g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (lineariz…
PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field
Haixia Bi, Jing Yao, Zhiqiang Wei +2
Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications. However, it is still a challenging task…
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…
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
Jing Yao, Danfeng Hong, Jocelyn Chanussot +3
The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR). Yet the development of unsupervised deep networks remai…
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…