activity
20192021
most citedDeep Learning for Hyperspectral Image Classification: An Overview

1.9k citations · 1.9k across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV20211 cited

Hybrid Mutimodal Fusion for Dimensional Emotion Recognition

Ziyu Ma, Fuyan Ma, Bin Sun +1

In this paper, we extensively present our solutions for the MuSe-Stress sub-challenge and the MuSe-Physio sub-challenge of Multimodal Sentiment Challenge (MuSe) 2021. The goal of M…

cs.CV2020

Fusion of Dual Spatial Information for Hyperspectral Image Classification

Puhong Duan, Pedram Ghamisi, Xudong Kang +3

The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral imagery has led to significant improvements in terms of classification performance.…

eess.IV20207 cited

Recent Advances and New Guidelines on Hyperspectral and Multispectral Image Fusion

Renwei Dian, Shutao Li, Bin Sun +1

Hyperspectral image (HSI) with high spectral resolution often suffers from low spatial resolution owing to the limitations of imaging sensors. Image fusion is an effective and econ…

eess.IV20192 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…

eess.IV20191.9k cited

Deep Learning for Hyperspectral Image Classification: An Overview

Shutao Li, Weiwei Song, Leyuan Fang +3

Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate clas…

cs.CV2019

Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images

Weiwei Song, Shutao Li, Jon Atli Benediktsson

Driven by the urgent demand for managing remote sensing big data, large-scale remote sensing image retrieval (RSIR) attracts increasing attention in the remote sensing field. In ge…