1.7k citations · 2.6k across the 11 of their papers we have counts for
12 papers
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…
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…
Multimodal Fusion Transformer for Remote Sensing Image Classification
Swalpa Kumar Roy, Ankur Deria, Danfeng Hong +3
Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs). As a result, m…
SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers
Danfeng Hong, Zhu Han, Jing Yao +4
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepanci…
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…
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…
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…
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…
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…
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification
Qingshan Liu, Renlong Hang, Huihui Song +3
Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the cl…
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…
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
José M. Bioucas-Dias, Antonio Plaza, Nicolas Dobigeon +4
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than…