activity
20172022
most citedA3CLNN: Spatial, Spectral and Multiscale Attention ConvLSTM Neural Network for Multisource Remote Sensing Data Classification

149 citations · 155 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV20222 cited

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

Xin-Ru Feng, Heng-Chao Li, Rui Wang +3

Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix fa…

cs.CV2022149 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…

cs.CV2020

Image Segmentation Using Deep Learning: A Survey

Shervin Minaee, Yuri Boykov, Fatih Porikli +3

Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveilla…

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.IV2018

Pansharpening via Detail Injection Based Convolutional Neural Networks

Lin He, Yizhou Rao, Jun Li +2

Pansharpening aims to fuse a multispectral (MS) image with an associated panchromatic (PAN) image, producing a composite image with the spectral resolution of the former and the sp…

cs.CV20172 cited

Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study

Pedram Ghamisi, Gabriele Cavallaro, Dan +3

In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework. Here, an extended self-dua…