SEG2CD: Parameter-Free Change Detection

• SEG2CD is a segmentation-to-change detection method that transforms standard encoder-decoder architectures using a zero-parameter, invertible feature exchange operator.
• It integrates Siamese, weight-sharing encoders with shared decoders to fuse bi-temporal features without explicit arithmetic differencing.
• Empirical benchmarks demonstrate competitive performance and theoretical guarantees of information preservation in remote sensing change detection.

SEG2CD (Segmentation-to-Change Detection) is a parameter-free mechanism that transforms standard encoder–decoder semantic segmentation architectures into competitive bi-temporal change detectors by inserting a zero-parameter feature-exchange operator between Siamese encoders and shared decoders. SEG2CD is a product of the SEED (Siamese Encoder–Exchange–Decoder) paradigm and demonstrates that information-preserving feature exchange suffices for high-quality remote sensing change detection, outperforming or matching more complex fusion-based methods across multiple benchmarks and backbones (Dong et al., 12 Jan 2026).

1. Conceptual Overview and Motivation

SEG2CD originates from the observation that most prevalent change detection networks employ explicit arithmetic differencing—such as subtraction or concatenation—between bi-temporal feature representations. This approach often introduces additional parameters and can discard discriminative information. By contrast, SEG2CD leverages a parameter-free, invertible feature-exchange operation formalized as a permutation operator, enabling the conversion of any off-the-shelf encoder–decoder segmentation model (e.g., U-Net, DeepLabV3+) into a change detector without introducing new trainable parameters or explicit feature differencing.

Under the SEED framework, SEG2CD couples two weight-sharing encoder–decoder branches (Siamese) that process the paired images, combines their intermediate latent states through exchange, and yields bi-temporal outputs for change prediction. The method’s novelty lies in its simplicity, interpretability, and theoretical information preservation.

2. Architectural Formulation

The SEG2CD recipe is formalized as follows:

• Given two registered images $I_A$ and %%%%1%%%%, each is passed through identical encoders $E_\psi$ (weights shared), producing multi-level feature pyramids $\{X_A^\ell\}_{\ell=1}^L$ and $\{X_B^\ell\}_{\ell=1}^L$.
• At each level $\ell$, feature pairs $(X_A^\ell, X_B^\ell)$ are processed by a zero-parameter permutation operator $\Pi^\ell$, yielding $$(\tilde{X}_A^\ell, \tilde{X}_B^\ell) = \Pi^\ell(X_A^\ell, X_B^\ell)$$.
• Optionally, a shared neck $F_\theta$ (e.g., FPN) processes the exchanged features.
• Both branches proceed through shared, duplicated decoders $D_\phi$, producing predictions $Z_A$ and $Z_B$.
• During training, binary cross-entropy losses for each branch are summed: $$\mathcal{L} = \mathcal{L}_{\rm BCE}(Z_A, Y_{\rm true}) + \mathcal{L}_{\rm BCE}(Z_B, Y_{\rm true})$$.
• During inference, logits from both branches are averaged and passed through sigmoid: $\hat{Y} = \sigma\bigl(\frac{1}{2}(Z_A + Z_B)\bigr)$.

Feature Exchange Operator

For each feature map level, a stochastic channel- (or layer-)wise mask samples an exchange pattern:

mask = sample Bernoulli(p) # shape (C,) mask = mask.view(1,C,1,1).expand(B,C,H,W) inv = 1 - mask xA_out = mask * xB + inv * xA xB_out = mask * xA + inv * xB return xA_out, xB_out

Spatial and layer-level exchanges are analogously defined, providing operational flexibility.

3. Mathematical Properties and Theory

Permutation Formalization

Consider flattened feature vectors $\mathbf{x}, \mathbf{y} \in \mathbb{R}^m$. The exchange operation stacks these as $\mathbf{z} = [\mathbf{x}; \mathbf{y}]$ and applies an orthogonal permutation operator $P_{\rm swap}$, parameterized by binary mask $\epsilon$:

$$D = \mathrm{diag}(\epsilon_1, ..., \epsilon_m),\quad E = I_m - D$$

$$P_{\rm swap} = \begin{pmatrix} E & D \ D & E \end{pmatrix}$$

Resulting in $(\mathbf{x}', \mathbf{y}')$ where each feature is swapped or kept based on $\epsilon$.

Information Preservation

SEG2CD’s exchange operator is invertible and isometric, thus preserving:

• Mutual information with pixel-level change labels: $$I\left(P_{\rm swap}\mathbf{Z}; Y\right) = I\left(\mathbf{Z}; Y\right)$$
• Bayes-optimal risk: $$\mathcal{R}^*\left(P_{\rm swap}\mathbf{Z} \to Y\right) = \mathcal{R}^*\left(\mathbf{Z} \to Y\right)$$

Comparison to Arithmetic Fusion

Table 1 summarizes core contrasts of feature exchange versus common competing fusion operations:

Fusion Type	Invertible	Rank-Preserving	Mutual Info Preserved
Addition	No	No ($m$ of $2m$)	No
Subtraction	No	No	No
Concatenation+$1\times1$	No	No ($<2C$ of $2C$ when compressed)	No
Exchange (SEG2CD)	Yes	Yes	Yes

Arithmetic fusions (addition/subtraction/linear compression) are non-invertible, lose mutual information ($I(U;Y) < I(\mathbf{Z}; Y)$), and can result in degraded conditioning and optimization.

4. Implementation and Backbone Integration

SEG2CD applies zero-parameter exchange to a wide range of backbones:

• Swin Transformer V2-Base (SwinTv2)
• EfficientNet-B4
• ResNet-50

Integration steps:

1. Convert the encoder to a Siamese, weight-sharing pair.
2. Insert channel-, spatial-, or layer-exchange blocks after each encoder stage or feature level.
3. Optionally, process exchanged features with a shared feature pyramid network.
4. Utilize identical decoders per branch, sharing parameters.
5. Dual predictions enable hybrid loss strategies and robust inference, with the option for single-decoder deployment at test time for $\sim$25% reduced FLOPs and minimal IoU loss ($\approx$0.2–0.3 points).

5. Training, Datasets, and Empirical Results

Protocol

• Data augmentation: random rotations, flips, photometric distortions.
• Optimization: AdamW, $1\mathrm{e}{-4}$ learning rate and weight decay.
• Exchange probabilities: $p_L = p_C = p_S = 0.5$.
• Typical batch: $8$–$16$, $50$–$100$ epochs.

Benchmarks

• SYSU-CD (20K pairs)
• LEVIR-CD (building change)
• PX-CLCD
• WaterCD
• CDD (seasonal change)

Performance Table (SwinTv2 Backbone)

Dataset	IoU (SEED)	F1 (SEED)
SYSU-CD	70.91	82.98
LEVIR-CD	86.25	92.62
PX-CLCD	95.50	97.70
WaterCD	84.64	91.68
CDD	97.11	98.53

Notably, SEG2CD conversion on LEVIR-CD achieves:

• AFENet (ResNet-18): IoU 85.65, F1 92.27
• DeepLabV3+ (Xception-65): IoU 84.76, F1 91.75

On SYSU-CD:

• AFENet (ResNet-18): IoU 69.98, F1 82.34
• DeepLabV3+ (Xception-65): IoU 68.53, F1 81.33

These results demonstrate the competitiveness of standard segmentation architectures enhanced solely with exchange.

6. Practical Considerations and Extensions

• Exchange variants: layer-exchange (LE), channel-exchange (CE), spatial-exchange (SE) are effective; $p\approx0.5$ is recommended.
• Computational cost: By default, dual-branch decoders double FLOPs; use single-decoder inference for efficiency trade-off.
• Limiting factors: Assumes perfect co-registration (tolerates 2–4 pixel misalignment). Cannot discover change types absent from training data. Misalignment is not alleviated—pre-alignment modules may be required for highly misregistered inputs.
• Self-supervised extensions: SEG2CD/SEED can be integrated with masked autoencoder (MAE) pretraining, exchanging tokens during reconstruction.
• Lightweight deployment: MobileNet or ShuffleNet backbones with channel-exchange yield ultra-lightweight change detectors.

7. Impact and Theoretical Significance

SEG2CD provides rigorous, theory-backed evidence supporting the sufficiency of invertible exchange in bi-temporal information fusion for semantic change detection. By unifying segmentation and change detection under a single, parameter-neutral architectural kernel, it both simplifies implementation and enhances interpretability. Its strong empirical benchmark results, broad backbone compatibility, and theoretical guarantees on information and risk preservation establish SEG2CD as a robust framework within the domain of remote-sensing change detection (Dong et al., 12 Jan 2026).