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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…
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…
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…