Differential Attention Orientated Cascade Network for Infrared Small Target Detection

Infrared small target detection from complex backgrounds is increasingly vital for military and civilian fields. Nonetheless, most of the existing methods are too restrictive to portray infrared targets from multidimensional and omnidirectional. In this article, we propose a low-rank differential ca...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 9253 - 9265
Main Authors: Tang, Wenjuan, Dai, Qun, Hao, Fan
Format: Journal Article
Language:English
Published: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Clutter
Datasets
Deep learning
differential attention
Feature extraction
feature fusion
Feature maps
Infrared imagery
infrared small target detection
Modules
Multidimensional methods
Object detection
Physical properties
Principal components analysis
Semantics
Sparse matrices
Target detection
Task analysis
Tensors
ISSN:1939-1404, 2151-1535
Online Access:Get full text
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Summary:Infrared small target detection from complex backgrounds is increasingly vital for military and civilian fields. Nonetheless, most of the existing methods are too restrictive to portray infrared targets from multidimensional and omnidirectional. In this article, we propose a low-rank differential cascade network (LDCNet) to integrate the physical properties and deep cascade features of infrared images. First, the cascade feature extraction module is designed via a multilevel coplanar cascade encoder-decoder structure, which integrates the deep-level and low-level features of infrared targets and backgrounds. Then, to provide a better understanding of the context capture of the scene, the differential attention mechanism based on the change differential analysis and robust principal component analysis is introduced. Finally, the multilevel feature fusion module is designed to adaptively integrate the spatial and semantic information of different depth feature maps to predict the final detection result. During the research, a new maritime small targets detection dataset is also constructed. Experimental results compared with other related methods on three datasets have demonstrated the effectiveness of LDCNet.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2024.3393238