基于多尺度融合的遥感图像变化检测模型

doi:10.13229/j.cnki.jdxbgxb.20221340

Abstract

Abstract:

The remote sensing image change detection model which is based on multi-scale fusion is proposed to accurately identify the change region of the bi-temporal remote sensing images. First， a multi-scale input pyramid is constructed in the feature extraction stage to receive multi-layer receptive fields and enhance the perception of all information. Then， in order to make a tradeoff between locating the changing area and mining details， the multi-scale calculation is carried out for deep difference features. Finally， the semantic change information can be identified and retained to a great extent by integrating the different feature results of the network. The experimental results show that the proposed model has good performance in both subjective evaluation and objective indexes.

Key words: change detection, multi-scale fusion, remote sensing image, deep learning

CLC Number:

TP391

Xiong-fei LI,Zi-xuan SONG,Rui ZHU,Xiao-li ZHANG. Remote sensing change detection model based on multi⁃scale fusion[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(2): 516-523.

Figures/Tables 5

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模型	LEVIR-CD					WHU
模型	Pre	Rec	F₁	OA	KC	Pre	Rec	F₁	OA	KC
FC-EF	0.8509	0.7401	0.7916	0.9802	0.7813	0.7673	0.8936	0.8256	0.9838	0.8172
FC-Siam-diff	0.8500	0.8233	0.8365	0.9836	0.8278	0.8134	0.8931	0.8514	0.9866	0.8444
FC-Siam-conc	0.8363	0.8239	0.8300	0.9828	0.8210	0.8142	0.8987	0.8544	0.9869	0.8475
SNUNet-CD	0.9049	0.8498	0.8765	0.9878	0.8701	0.8251	0.8898	0.8562	0.9872	0.8495
MSOF	0.8784	0.8680	0.8732	0.9872	0.8664	0.9471	0.8895	0.9174	0.9931	0.9138
STANet	0.8501	0.9138	0.8808	0.9874	0.8741	0.7936	0.9120	0.8487	0.9861	0.8414
本文	0.8852	0.9090	0.8969	0.9894	0.8913	0.9176	0.9202	0.9189	0.9931	0.9153

模型	Pre	Rec	F₁	OA	KC
去除输入金字塔	0.8872	0.8993	0.8932	0.9890	0.8874
去除深度差异特征多尺度计算	0.8817	0.9071	0.8942	0.9891	0.8885
去除多级差异特征融合	0.8860	0.8986	0.8923	0.9889	0.8865
本文	0.8852	0.9090	0.8969	0.9894	0.8913

Remote sensing change detection model based on multi⁃scale fusion

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