Multiscale Diff-Changed Feature Fusion Network for Hyperspectral Image Change Detection

For hyperspectral image (HSI) change detection (CD), multiscale features are usually used to construct the detection models. However, the existing studies only consider the multiscale features containing changed and unchanged components, which is difficult to represent the subtle changes between bit...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 61; s. 1 - 13
Hlavní autoři: Luo, Fulin, Zhou, Tianyuan, Liu, Jiamin, Guo, Tan, Gong, Xiuwen, Ren, Jinchang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Attention fusion
Change detection
change detection (CD)
Coders
Components
convolutional encoder-decoder network
Convolutional neural networks
Correlation
Data mining
Detection
Feature extraction
Fuses
hyperspectral image (HSI)
Hyperspectral imaging
Modules
multiscale features
Representations
Task analysis
Temporal variations
ISSN:0196-2892, 1558-0644
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:For hyperspectral image (HSI) change detection (CD), multiscale features are usually used to construct the detection models. However, the existing studies only consider the multiscale features containing changed and unchanged components, which is difficult to represent the subtle changes between bitemporal HSIs in each scale. To address this problem, we propose a multiscale diff-changed feature fusion network (MSDFFN) for HSI CD, which improves the ability of feature representation by learning the refined change components between bitemporal HSIs under different scales. In this network, a temporal feature encoder-decoder subnetwork, which combines a reduced inception (RI) module and a cross-layer attention module to highlight the significant features, is designed to extract the temporal features of HSIs. A bidirectional diff-changed feature representation (BDFR) module is proposed to learn the fine changed features of bitemporal HSIs at various scales to enhance the discriminative performance of the subtle change. A multiscale attention fusion (MSAF) module is developed to adaptively fuse the changed features of various scales. The proposed method can not only discover the subtle change in bitemporal HSIs but also improve the discriminating power for HSI CD. Experimental results on three HSI datasets show that MSDFFN outperforms a few state-of-the-art methods.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3241097