A Table for a systematic review of UNet-Like change detection. For the complete article, please refer to C. Wu, L. Zhang, B. Du et al., “UNet-Like Remote Sensing Change Detection: A review of current models and research directions,” IEEE Geoscience and Remote Sensing Magazine, pp. 2-31, 2024. (https://ieeexplore.ieee.org/abstract/document/10616141)

APPENDIX TABLE I SYSTEMATIC REVIEW OF UNET-LIKE CHANGE DETECTION MODELS CORRESPONDING TO DIFFRENT BLOCKS

	Name	Year	Encoder Structure	Symmetry	Encoder module	Feature to Decoder	Skip-connection	Data fusion	Loss
1	FC-EF[1]	2018	EF	Sym	Conv	EF	S-C	EF	CE
2	FC-Siam-Conc[1]	2018	Siam	Sym	Conv	T1	S-C	Conc	CE
3	FC-Siam-Diff[1]	2018	Siam	Sym	Conv	T1	S-C	Dif	CE
4	Jaturapitpornchai, et al. [2]	2019	EF	Sym	Conv	EF	S-C	EF	wBCE
5	FCN-PP[3]	2019	EF + Diff + Multivariate morphological reconstruction (MMR)	Sym	Conv	EF + Pyramid pooling (PP)	S-C	EF	wCE
6	Peng, et al. [4]	2019	EF	Sym	Res	EF	UNet++	EF	wBCE + Dice
7	CDGAN[5]	2020	Siam	Sym	Conv	Conc	S-C	Conc	GAN
8	PGA-SiamNet[6]	2020	Siam	Sym	ASPP	Co-attention	S-C	Change residual process	BCE
9	Liu, et al. [7]	2020	EF	Sym	Depthwise separable conv	EF	S-C	EF	BCE + Dice
10	DTCDSCN[8]	2020	Siam	Sym	Squeeze and excitation block (SE Block)	Dif + Spatial pyramid pooling module	S-C	Dif	wBCE + Change detection loss (CDL) + Deep supervision + Multitask
11	Peng, et al. [9]	2020	EF	Sym	Res	EF	Attention + UNet++	EF	wBCE + GAN (Segmentation adversarial loss + Entropy adversarial loss)
12	DDCNN[10]	2020	EF	Sym	Res	EF	UNet++	EF + Difference enhancement	BCE
13	Sun, et al. [11]	2020	EF	Sym	Conv	EF	S-C	EF	Multi-task
14	IFN[12]	2020	Siam	Sym	Conv	Conc	S-C	Conc	BCE + Dice + Deep supervision
15	Yang, et al. [13]	2021	Siam	Sym	Res	Conc + High-Level feature fusion block (HFFB)	S-C	Conc + Fusion block (FB)	CE
16	HDFNet[14]	2021	Siam	Sym	Conv	Fusion Steam + Conv	S-C	Conc	Focal + Multilevel supervision
17	Adriano, et al. [15]	2021	Pseudo-Siam	Sym	Conv	Conc + Attention gate	S-C	Conc + Attention gate	CE
18	DSA-Net[16]	2021	Siam	Sym	Strided conv + Attention guided cross-layer addition (ACLA)	Conc + ASPP	S-C	Conc + Spatial attention mechanism (SAM)	Deep supervision + BCD
19	SNUNet-CD[17]	2021	Siam	Sym	Res	T1	UNet++	Conc	Ensemble channel attention module (ECAM) + wCE +Dice
20	MSPSNet[18]	2021	Siam	Sym	Res	Conc + Parallel convolutional structure (PCS)	S-C	Conc + Parallel convolutional structure (PCS)	wCE + Dice
21	MASNet[19]	2021	Siam	Sym	Selective kernel convolution (SKConv)	Conc + Attention feature fusion module (AFFM)	S-C	Conc + Attention feature fusion module (AFFM)	BCE
22	DP-CD-Net[20]	2021	Pseduo-Siam	Sym	Res	Diff	S-C + Decoder S-C	Diff	CE + Auxiliary loss (AUX loss)
23	DTCDN[21]	2021	EF + Translation	Sym	Depthwise & pointwise Conv	EF	UNet++	EF	Weighted Focal + Deep supervision
24	Li, et al. [22]	2021	EF	Sym	Conv + ASPP	EF	S-C	Conc	wBCE + Dice
25	DCFF-Net[23]	2021	EF + Siam	Sym	Conv	EF	UNet++	Conc + Multi-features fusion (FF) module	BCE + Self-adjusting dice loss (SADICE)
26	Multitask L-Unet[24]	2021	Time-series input + Siam	Sym	Conv	Long short-term memory (LSTM)	S-C	Long short-term memory (LSTM)	CE + Multi-task
27	SCDNET[25]	2021	Siam	Sym	Res	T1/T2 + Multi-scale atrous convolution (MAC)	S-C	Diff + Attention	CE + Deep Fusion
28	ADS-Net[26]	2021	Siam	non-Sym	Conv	Dif + Conc + Attention	Weighted sum	Diff + Conc + Attention	Deep supervision + Focal
29	BASNet[27]	2021	Siam	Sym	Res	Conc + Multiscale-paired fusion module (MPFM) + Spatial pyramid pooling module (SPPM)	S-C + Guiding flows + Max pooling	Conc + Multiscale-paired fusion module (MPFM)	BCE + Multi-level feature aggregation + Structural similarity (SSIM)
30	DCA-Net[28]	2021	Siam	Sym	Res	Dual correlation attention module (DCAM)	S-C	Dual correlation attention module (DCAM)	Bounding box regression loss + Change confidence prediction loss
31	DifUnet++[29]	2021	Siam + EF	Sym	Conv	Dif + Conc	UNet++	Dif + Conc	Focal
32	CLNet[30]	2021	EF	Sym	Cross layer blocks (CLB)	EF	S-C	EF	wBCE + Dice
33	Siam-GL[31]	2021	Siam	Sym	Conv	Conc + Binary change mask	S-C	Conc	Weighted softmax + Multi-class change detection
34	CS-HSNet[32]	2021	Siam	non-Sym	Cross-Siamese Res2Net (CSRes2Net) module	Hierarchical-split attention block + Conc	Conc	Hierarchical-split attention block + Conc	nan
35	EGRCNN[33]	2022	Siam	Sym	Conv	Difference analysis	S-C	Difference analysis	Focal + Deep supervision + Edge constraint
36	UCDNet[34]	2022	Siam	Sym	Conv + Modified residual connection	Conc	S-C	Conc + New spatial pyramid pooling (NSPP) block	wCE + modified kappa loss
37	FCCDN[35]	2022	Siam	Sym	Squeeze and excitation ResNet (SE-ResNet)	Nonlocal feature pyramid network (NL-FPN) + Dense fusion (DFM)	S-C	Dense fusion (DFM)	Self-supervised learning + Multi-task + BCE + Dice
38	Siamese_AUNet[36]	2022	Siam	Sym	Conv + Feature attention model (FAM)	ASPP + Conc	S-C	Conc	BCE
39	ISNet[37]	2022	Siam	Sym	Res + Channel attention (CA)	Margin maximization (MM) + Spatial attention (SA)	S-C	Margin maximization (MM) + Spatial attention (SA)	Dice + CE
40	MCDnet [38]	2022	Siam	Sym	Conv + Bidirectional feature pyramid network (BiFPN)	Change feature fusion module (CFFM)	S-C	Change feature fusion module (CFFM)	Multi-task + Change contrast loss (CCL)
41	LWCDNet[39]	2022	EF	Sym	Artificial padding convolution (APC)	EF + Convolutional block attention module (CBAM)	S-C	EF	wCE + Lovász
42	ForkNet[40]	2022	Siam	Sym	Conv + Cross-resolution attention module (CRAM) + Feature pyramid network (FPN)	Diff	S-C + Feature pyramid network (FPN)	Diff	Pyramid Tversky loss + Focal
43	Forest-CD[41]	2022	Siam	Sym	Transformer	Conc + Pyramid pooling module (PPM)	S-C	Conc	Multi-layer stacking + OCR + Deep supervision + Focal + Dice
44	LGSAA-Net[42]	2022	Siam + Diff Image	Sym	Conv	Diff + Conc + Scale-adaptive attention (SAA) + Multilayer perceptron based on patches embedding (MLPPE)	S-C	Diff + Conc + Scale-adaptive attention (SAA) + Multilayer perceptron based on patches embedding (MLPPE)	nan
45	DARNet[43]	2022	Siam	Sym	Conv	Hybrid attention module (HAM)	Densely Connected	Hybrid attention module (HAM)	Deep supervision + BCE + Dice
46	EUNet[44]	2022	Siam	Sym	Encoder with efficient convolution module (EECM)	Fusion operation (FO)	UNet++	Fusion operation (FO)	Multi-layer + wCE + Dice
47	FCDNet[45]	2022	Siam	Sym	Depthwise over-parameterized convolutional layer (DOConv)	Diff + Multireceptive field position enhancement module (MRPEM)	Densely Connected	Diff + Multireceptive field position enhancement module (MRPEM)	wCE + Dice
48	SwinSUNet[46]	2022	Siam	Sym	Swin Transformer	Conc + Swin Transformer	S-C	Conc	nan
49	EGDE-Net[47]	2022	Siam	Sym	Edge-guided Transformer blocks (EGTB）	Dif + Feature differential enhancement modules (FDEM)	S-C	Dif + Feature differential enhancement modules (FDEM)	BCE + Edge loss
50	SMD-Net[48]	2022	Siam	Sym	Res	Conc + Siamese residual multi-kernel pooling module (SRMP)	S-C	Conc + Feature difference module (FDM)	BCE + Tversky
51	SSANet[49]	2022	EF + Dif	Sym	Multicore channel-aligning attention (MCA) module + Feature differential reconfiguration (FDR) module	EF + Addition	S-C	EF + Addition	Batch balance contrast loss
52	DESSN[50]	2022	Siam	Sym	Asymmetric double convolution with Ghost (ADCG) + Difference enhancement (DE) module	Conc + Spatial–spectral nonlocal (SSN) module	S-C	Difference enhancement (DE) module	BCE
53	TransUNetCD [51]	2022	Siam	Sym	Conv	Embedding + Transformer	S-C	Conc	Difference enhance + Dice
54	Lv, et al. [52]	2022	Siam + Dif	Sym	Conv + Convolutional block attention module (CBAM)	Multiscale dilation convolution module (MDCM)	S-C	Conc	BCE
55	FHD[53]	2022	Siam	Sym	Mix Transformer (MiT)	Time-specific feature (TSF) + Hierarchical differentiation (HD) modules	S-C	Time-specific feature (TSF) + Hierarchical differentiation (HD) modules	CE
56	DPCC-Net[54]	2022	Siam	non-Sym	Conv	Dual-perspective fusion (DPF)	Conc	Dual-perspective fusion (DPF)	Tanimoto
57	CDENet[55]	2022	Siam	Sym	Conv	Transformer + Content difference enhancement module (CDEM)	S-C	Content difference enhancement module (CDEM)	BCE + Dice
58	HMCNet[56]	2022	Siam	Sym	Res	Multilayer perceptron (MLP)	S-C	Conc	wCE + Dice
59	STransUNet[57]	2022	Siam	Sym	Conv	Transformer + Cross-enhanced adaptive fusion (CEAF)	S-C	Cross-enhanced adaptive fusion (CEAF)	nan
60	DA-MSCDNet[58]	2022	Siam	Sym	Conv	Domain adaptation-based feature constraints + Dif	S-C	Dif	MK-MMD + Multi-layer stacking + BCE + Dice
61	HFA-Net[59]	2022	Siam	Sym	High frequency attention block (HFAB)	Conc	S-C	Conc	BCE
62	Li, et al. [60]	2022	Siam	non-Sym	Res	Temporal feature interaction module (TFIM)	Guided refinement module (GRM)	Temporal feature interaction module (TFIM)	BCE + Dice
63	SFCCD[61]	2022	Siam	Sym	Res	Multiscale feature fusion module (MSFF)	S-C	Global channel attention module (GCA) + Multiscale feature fusion module (MSFF)	Multi-task + BCE
64	PCFN[62]	2022	Siam	Sym	Res	Conc	S-C	Conc	Multi-task + Feature difference enhancement + wCE
65	DAFT[63]	2023	Siam	Sym	AFFormer	Multilayer Conc	S-C	Differential features enhancement module (DFEM)	BCE + Deep supervision
66	AMIO-Net[64]	2023	Siam	Sym	Conv + Multi-scale deep features	Pyramid Pooling Attention Module (PPAM) + Conc	S-C	Conc	CE + Dice
67	AFDE-Net[65]	2023	Siam	Sym	Conv	Dif	S-C	Dif + Ensemble spatial-channel attention fusion (ESCAF) module	wCE + Deep supervision
68	AFNUNET[66]	2023	EF	Sym	Res	EF	UNet++	EF	Multi-layer features + Adaptive fusion module (AFM) + BCE + Bray-curtis ordination
69	ConvTransNet[67]	2023	Siam	Sym	CNN-Transformer	Conc	S-C	Conc	wCE
70	A2Net[68]	2023	Siam	Sym	Res	Neighbor aggregation module (NAM) + Dif + Progressive change identifying module (PCIM)	S-C	Neighbor aggregation module (NAM) + Dif + Progressive change identifying module (PCIM)	BCE + Dice + Deep supervision
71	Lv, et al. [69]	2023	Siam	Sym	Conv	Conc + Position channel attention module (PCAM)	S-C	Conc + Multiscale information attention module (MIAM)	Change gradient guided module + CE
72	WNet[70]	2023	Siam	Sym	Deformable Residual (D-Res) + Swin-Transformer	Dif + Conc + CNN–Transformer fusion module (CTFM)	S-C	Dif + Conc + CNN–Transformer fusion module (CTFM)	CE + Dice
73	SSCFNet[71]	2023	Siam	non-Sym	Res	Dif + Spatial-spectral cross fusion module	Add	Dif + Spatial-spectral cross fusion module	BCE + Deep supervision
74	TCIANet[72]	2023	Siam	non-Sym	Res	Filter-based visual tokenizer (FVT) + Progressive sampling vision (PSM) + Transformer + Dif	Conc	Feature fusion module (FFM) + Contour-graph reasoning module (CGRM) + Dif	CE
75	HSSENet[73]	2023	Siam	Sym	Conv + Transformer	Conc + Spatiotemporal enhancement module (STEM)	S-C	Dif	Deep supervision + CE
76	SAGNet[74]	2023	Siam	non-Sym	Res	Global semantic aggregation module (GSAM)	Add	Dif + Cross-scale fusion module (CFM) + Conc + Bilateral feature fusion module (BFEM)	BCE
77	DMINet[75]	2023	Siam	non-Sym	Conv	Joint attention module + Dif + Conc	Incremental aggregation	Joint attention module +Dif + Conc	CE + Deep supervision
78	DGANet[76]	2023	Siam	non-Sym	Conv	Difference-guided aggregation module (DGAM)	Conc	Weighted metric module (WMM)	Batch-balanced contrastive loss (BCL) + Change magnitude contrastive loss (CMCL)
79	ACAHNet[77]	2023	Siam	Sym	Conv	Conc+ Semantic Generation + Asymmetric multihead crossover attention (AMCA)	S-C	Conc + Three branch aggregation (TBA)	wCE + Dice
80	FMCD[78]	2023	Siam	Sym	EfficientNet	Multilevel feature interaction module + Mix block	S-C	Mix attention block (MAB)	BCE + Domain adaption loss
81	TSNet[79]	2023	Siam + Conc	Sym	EfficientNet	ConvGRU + Dual channel attention module	S-C	ConvGRU + Dual channel attention module	BCE + Dice
82	MFSNet[80]	2023	Siam	Sym	Conv	Conc	UNet++	Conc	Deep supervision + BCE + Dice
83	HANet[81]	2023	Siam	Sym	Conv	Hierarchical Attention Network (HAN)	S-C	Hierarchical Attention Network (HAN)	wCE + Dice
84	T-Unet[82]	2023	Siam + Dif	Sym	Conv + Multi-branch spatial-spectral cross attention (MBSSCA) module	Multi-branch spatial-spectral cross attention (MBSSCA) module	S-C	Multi-branch spatial-spectral cross attention (MBSSCA) module	BCE + Dice
85	CGNet[83]	2023	Siam	Sym	Conv	Conc + Change guide module (CGM)	S-C	Conc + Change guide module (CGM)	CE + Deep supervision
86	SAAN[84]	2023	Siam	Sym	EfficientNet	Conc	S-C	Similarity-guided attention flow module	BCE + Dice + Deep supervision + Contrastive loss

*In the table, the abbreviations are explained as: early fusion (EF), Siamese structure (Siam), pseudo-Siamese structure (pseudo-Siam), symmetry (Sym), non-symmetry (Non-sym), convolution module (Conv), residual module (Res) module, concatenation (Conc), difference (Dif), features from one time (T1/T2), and skip-connection (S-C). These abbreviations mostly indicate the basic blocks for UNet-like change detection models.

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