Stack Attention-Pruning Aggregates Multiscale Graph Convolution Networks for Hyperspectral Remote Sensing Image Classification
The hyperspectral remote sensing images are classified by traditional neural networks methods can achieve promising performance, but only operate on regular square regions with fixed. This will lead to between neighborhood pixels have limitations in achieve long-distances joint interaction modeling...
Main Authors: | Na Liu, Bin Zhang, Qiuhuan Ma, Qingqing Zhu, Xiaoling Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9360820/ |
Similar Items
-
Multiscale Graph Sample and Aggregate Network With Context-Aware Learning for Hyperspectral Image Classification
by: Yao Ding, et al.
Published: (2021-01-01) -
A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing
by: Jinlin Zou, et al.
Published: (2019-03-01) -
Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification
by: Fei Tong, et al.
Published: (2017-08-01) -
Interactive Image Segmentation on Multiscale Appearances
by: Kun He, et al.
Published: (2018-01-01) -
Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction
by: Jinliang An, et al.
Published: (2019-06-01)