A lightweight false alarm suppression method in heterogeneous change detection

doi:10.23919/JSEE.2024.000086

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (4): 899-905.doi: 10.23919/JSEE.2024.000086

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles

A lightweight false alarm suppression method in heterogeneous change detection

Cong XU¹(), Zishu HE²(), Haicheng LIU¹^,*()

¹ School of Electronic and Information Engineering, Heilongjiang Institute of Engineering, Harbin 150026, China
² School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Received:2023-10-11 Accepted:2024-06-27 Online:2024-08-18 Published:2024-08-06
Contact: Haicheng LIU E-mail:xucong_0803@126.com;zshe@uestc.edu.cn;liuhaicheng@126.com
About author:
XU Cong was born in 1983. She received her B.S. and Ph.D. degrees in communication and information system from the University of Harbin Engineering University, Harbin, in 2006, and 2010, respectively. She is now working with the School of Electronic and Information Engineering in Heilongjiang Institute of Technology. Her research interests are intelligent processing of radar and remote sensing signals. E-mail: xucong_0803@126.com

HE Zishu was born in 1962. He received his B.S., M.S., and Ph.D. degrees in signal and information processing from the University of Electronic Science and Technology of China (UESTC), Chengdu, in 1984, 1988, and 2000, respectively. He is a professor in signal and information processing with the School of Information and Communication Engineering, UESTC. His research interests are in array signal processing, digital beamforming, the theory on multiple-input multiple-output (MIMO) communication and MIMO radar, adaptive signal processing, and channel estimation. E-mail: zshe@uestc.edu.cn

LIU Haicheng was born in 1979. He received his B.S degree in electronic information engineering from Northeast Agricultural University, Harbin, in 2003 and M.S. degree in electronic and communication engineering from Harbin Institute of Technology, Harbin, in 2012. He is now working with the School of Electronic and Information Engineering in Heilongjiang Institute of Technology. His research interests are signal processing and intelligent hardware. E-mail: liuhaicheng@126.com
Supported by:
This work was supported by the Natural Science Foundation of Heilongjiang Province (LH2022F049).

Abstract

Abstract:

Overlooking the issue of false alarm suppression in heterogeneous change detection leads to inferior detection performance. This paper proposes a method to handle false alarms in heterogeneous change detection. A lightweight network of two channels is bulit based on the combination of convolutional neural network (CNN) and graph convolutional network (GCN). CNNs learn feature difference maps of multitemporal images, and attention modules adaptively fuse CNN-based and graph-based features for different scales. GCNs with a new kernel filter adaptively distinguish between nodes with the same and those with different labels, generating change maps. Experimental evaluation on two datasets validates the efficacy of the proposed method in addressing false alarms.

Key words: convolutional neural network (CNN), graph convolutional network (GCN), heterogeneous change detection, lightweight, false alarm suppression

Cong XU, Zishu HE, Haicheng LIU. A lightweight false alarm suppression method in heterogeneous change detection[J]. Journal of Systems Engineering and Electronics, 2024, 35(4): 899-905.

Figures/Tables 14

Fig 1

Table 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

Fig 7

Table 2

Table 3

Fig 8

Fig 9

Table 4

Table 5

References 23

1	JIMENEZ-SIERRA D A, QUINTERO-OLAYA D A, ALVEAR-MUNOZ J C, et al Graph learning based on signal smoothness representation for homogeneous and heterogeneous change detection. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 4410416.
2	SUN Y L, LEI L, GUAN D D, et al Graph signal processing for heterogeneous change detection. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 4415823.
3	SUN Y L, LEI L, GUAN D D, et al Iterative robust graph for unsupervised change detection of heterogeneous remote sensing images. IEEE Trans. on Image Processing, 2021, 30, 6277- 6291. doi: 10.1109/TIP.2021.3093766
4	SUN Y L, LEI L, LI X, et al Nonlocal patch similarity based heterogeneous remote sensing change detection. Pattern Recognition, 2021, 109, 107598. doi: 10.1016/j.patcog.2020.107598
5	SUN Y L, LEI L, LI X, et al Structure consistency-based graph for unsupervised change detection with homogeneous and heterogeneous remote sensing images. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 4700221.
6	SUN Y L, LEI L, GUAN D D, et al Sparse-constrained adaptive structure consistency-based unsupervised image regression for heterogeneous remote-sensing change detection. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 4405814.
7	ZHANG M, SHI W Z A feature difference convolutional neural network-based change detection method. IEEE Trans. on Geoscience and Remote Sensing, 2020, 58 (10): 7232- 7246. doi: 10.1109/TGRS.2020.2981051
8	LUPPINO L T, KAMPFFMEYER M, BIANCHI F M, et al Deep image translation with an affinity-based change prior for unsupervised multimodal change detection. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 4700422.
9	CHEN H R X, WU C, DU B, et al Change detection in multisource VHR images via deep siamese convolutional multiple-layers recurrent neural network. IEEE Trans. on Geoscience and Remote Sensing, 2020, 58 (4): 2848- 2864. doi: 10.1109/TGRS.2019.2956756
10	SUN B Y, LIU Q S, YUAN N Z, et al Spectral token guidance transformer for multisource images change detection. IEEE Journal of Selective Topics in Applied Earth Observations and Remote Sensing, 2023, 16, 2559- 2572. doi: 10.1109/JSTARS.2023.3251962
11	WU Y, LI J H, YUAN Y Z, et al Commonality autoencoder: learning common features for change detection from heterogeneous images. IEEE Trans. on Image Processing, 2022, 33 (9): 4257- 4270.
12	CHEN H R X, YOKOYA N, WU C, et al Unsupervised multimodal change detection based on structural relationship graph representation learning. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5635318.
13	WU J Z, LI B, QIN Y, et al A multiscale graph convolutional network for change detection in homogeneous and heterogeneous remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 2021, 105, 102615. doi: 10.1016/j.jag.2021.102615
14	JIA M, ZHANG C, LV Z Y, et al Bipartite adversarial autoencoders with structural self-similarity for unsupervised heterogeneous remote sensing image change detection. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 6515705.
15	CHEN H, HE F C, LIU J M Heterogeneous images change detection based on iterative joint global-local translation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15, 9680- 9698. doi: 10.1109/JSTARS.2022.3192251
16	ZHU S Y, SONG Y H, ZHANG Y, et al ECFNet: a siamese network with fewer FPs and fewer FNs for change detection of remote-sensing images. IEEE Geoscience and Remote Sensing Letters, 2023, 20, 6001005.
17	YU C P, XIONG W, LI X Q, et al Deep convolutional neural network for meteorology target detection in airborne weather radar images. Journal of Systems Engineering and Electronics, 2023, 34 (5): 1147- 1157.
18	FENG W, LONG Y J, WANG S, et al A review of addressing class noise problems of remote sensing classification. Journal of Systems Engineering and Electronics, 2023, 34 (1): 36- 46.
19	YANG R, DAI W R, LI C L, et al Tackling over-smoothing in graph convolutional networks with EM-based joint topology optimization and node classification. IEEE Trans. on Signal and Information Processing over Networks, 2023, 9, 123- 139. doi: 10.1109/TSIPN.2023.3244112
20	PENG Z H, LIU H, JIA Y H, et al. Attention-driven graph clustering network. Proc. of the 29th ACM International Conference on Multimedia, 2021: 935–943.
21	WU L R, LIN H T, HU B Z, et al Beyond homophily and homogeneity assumption: relation-based frequency adaptive graph neural networks. IEEE Trans. on Neural Networks and Learning Systems, 2023, 35 (6): 8497- 8509.
22	XU C, LIU B S, HE Z S A new method for false alarm suppression in heterogeneous change detection. Remote Sensing, 2023, 15 (7): 1745. doi: 10.3390/rs15071745
23	ZHENG X L, GUAN D D, LI B J, et al Change smoothness-based signal decomposition method for multimodal change detection. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 2507605.

Type	Output
Conv+ReLU	224×224×64
Conv+ReLU	224×224×64
Max pooling	112×112×64
Conv+ReLU	112×112×128
Conv+ReLU	112×112×128
Max pooling	56×56×128
Conv+ReLU	56×56×256
Conv+ReLU	56×56×256
Conv+ReLU	56×56×256
Feature difference	56×56×256
Upsampling	224×224×256
Feature difference	112×112×128
Upsampling	224×224×128
Feature difference	224×224×64

Method	Performance
Method	AUC	FA	OA	MD
The proposed method with modification	0.847	0.161	0.842	0.237
The proposed method	0.970	0.076	0.969	0.113

Method	Performance
Method	AUC	FA	OA	MD
The proposed method with modification	0.833	0.177	0.821	0.221
The proposed method	0.963	0.066	0.973	0.103

Method	Performance
Method	AUC	FA	OA	MD
INLPG	0.938	0.174	0.942	0.237
FDCCN	0.953	0.162	0.963	0.228
The proposed method	0.969	0.093	0.979	0.114

Method	Performance
Method	AUC	FA	OA	MD
INLPG	0.955	0.074	0.957	0.161
FDCCN	0.962	0.057	0.972	0.152
The proposed method	0.966	0.062	0.977	0.102

A lightweight false alarm suppression method in heterogeneous change detection

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 14

References 23

Related Articles 12

Recommended Articles

Metrics

Comments

[1]	Jinyang CHEN, Xuhua WANG, Xian CHEN. Track correlation algorithm based on CNN-LSTM for swarm targets [J]. Journal of Systems Engineering and Electronics, 2024, 35(2): 417-429.
[2]	Chaopeng YU, Wei XIONG, Xiaoqing LI, Lei DONG. Deep convolutional neural network for meteorology target detection in airborne weather radar images [J]. Journal of Systems Engineering and Electronics, 2023, 34(5): 1147-1157.
[3]	Qihai YAO, Yong WANG, Yixin YANG. Range estimation of few-shot underwater sound source in shallow water based on transfer learning and residual CNN [J]. Journal of Systems Engineering and Electronics, 2023, 34(4): 839-850.
[4]	Hao DU, Wei WANG, Xuerao WANG, Yuanda WANG. Autonomous landing scene recognition based on transfer learning for drones [J]. Journal of Systems Engineering and Electronics, 2023, 34(1): 28-35.
[5]	Zhengliang ZHU, Degui YANG, Junchao ZHANG, Feng TONG. Dataset of human motion status using IR-UWB through-wall radar [J]. Journal of Systems Engineering and Electronics, 2021, 32(5): 1083-1096.
[6]	Tao YE, Zongyang ZHAO, Jun ZHANG, Xinghua CHAI, Fuqiang ZHOU. Low-altitude small-sized object detection using lightweight feature-enhanced convolutional neural network [J]. Journal of Systems Engineering and Electronics, 2021, 32(4): 841-853.
[7]	Wantian WANG, Ziyue TANG, Yichang CHEN, Yongjian SUN. Parity recognition of blade number and manoeuvre intention classification algorithm of rotor target based on micro-Doppler features using CNN [J]. Journal of Systems Engineering and Electronics, 2020, 31(5): 884-889.
[8]	Long ZHOU, SuyuanX WEI, Zhongma CUI, Jiaqi FANG, Xiaoting YANG, Wei DING. Lira-YOLO: a lightweight model for ship detection in radar images [J]. Journal of Systems Engineering and Electronics, 2020, 31(5): 950-956.
[9]	Binquan LI, Xiaohui HU. Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach [J]. Journal of Systems Engineering and Electronics, 2019, 30(2): 238-244.
[10]	Baojun ZHAO, Boya ZHAO, Linbo TANG, Wenzheng WANG, Chen WU. Multi-scale object detection by top-down and bottom-up feature pyramid network [J]. Journal of Systems Engineering and Electronics, 2019, 30(1): 1-12.
[11]	Liangkui LIN, Shaoyou WANG, Zhongxing TANG. Using deep learning to detect small targets in infrared oversampling images [J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 947-952.
[12]	Bendong Zhao, Huanzhang Lu, Shangfeng Chen, Junliang Liu, and Dongya Wu. Convolutional neural networks for time series classification [J]. Systems Engineering and Electronics, 2017, 28(1): 162-.