activity
20152022
most citedRobust Collaborative Nonnegative Matrix Factorization For Hyperspectral Unmixing (R-CoNMF)

193 citations · 486 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2022★ 79 cited

Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives

Jun Yue, Leyuan Fang, Pedram Ghamisi +4

In recent years, supervised learning has been widely used in various tasks of optical remote sensing image understanding, including remote sensing image classification, pixel-wise…

cs.CV2022★ 149 cited

A3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification

Heng-Chao Li, Wen-Shuai Hu, Wei Li +3

The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing. In this paper, we propose a new…

eess.IV2021★ 63 cited

A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features

Jun Li, Zhaocong Wu, Zhongwen Hu +5

Clouds are a very important factor in the availability of optical remote sensing images. Recently, deep learning-based cloud detection methods have surpassed classical methods base…

eess.IV2019★ 2 cited

Naive Gabor Networks for Hyperspectral Image Classification

Chenying Liu, Jun Li, Lin He +3

Recently, many convolutional neural network (CNN) methods have been designed for hyperspectral image (HSI) classification since CNNs are able to produce good representations of dat…

math.OC2015★ 193 cited

Robust Collaborative Nonnegative Matrix Factorization For Hyperspectral Unmixing (R-CoNMF)

Jun Li, Jose M. Bioucas-Dias, Antonio Plaza +1

The recently introduced collaborative nonnegative matrix factorization (CoNMF) algorithm was conceived to simultaneously estimate the number of endmembers, the mixing matrix, and t…