Paper Info
Title
Remote Sensing Time Series Classification Based On Self-Attention Mechanism And Time Sequence Enhancement
Abstract
Nowadays, in the field of data mining, time series data analysis is a very important and challenging subject. This is especially true for time series remote sensing classification. The classification of remote sensing images is an important source of information for land resource planning and management, rational development, and protection. Many experts and scholars have proposed various methods to classify time series data, but when these methods are applied to real remote sensing time series data, there are some deficiencies in classification accuracy. Based on previous experience and the processing methods of time series in other fields, we propose a neural network model based on a self-attention mechanism and time sequence enhancement to classify real remote sensing time series data. The model is mainly divided into five parts: (1) memory feature extraction in subsequence blocks; (2) self-attention layer among blocks; (3) time sequence enhancement; (4) spectral sequence relationship extraction; and (5) a simplified ResNet neural network. The model can simultaneously consider the three characteristics of time series local information, global information, and spectral series relationship information to realize the classification of remote sensing time series. Good experimental results have been obtained by using our model.
Year
DOI
Venue
2021
10.3390/rs13091804
REMOTE SENSING
Keywords
DocType
Volume
self-attention, ResNet, subsequence, time sequence enhancement, spectral relationship
Journal
13
Issue
Citations 
PageRank 
9
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Jingwei Liu12010.65
Jining Yan233.78
Lizhe Wang32973191.46
Liang Huang400.34
Haixu He500.68
Hong Liu600.34