*4.4. Ablation Study*

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To verify the validity of IIB, SSA, and PA, we conduct an ablation study on the WV2 validation dataset. In this subsection, the number of IIG and the number of RCAB in each IIG are set to four and two, respectively. The results of ablation experiments are given in Table 2. The meanings of each method are as follows:

	- Base+PA+SSA+IIB: The method proposed in this paper (SSIN) that turn on the interaction connection in each IIB on the basis of Base+PA+SSA.

For the baseline, we remove PA from IF and turn off the interaction connection in SSA and IIB. By doing this, spatial and spectral information can only be processed separately, and the spatial and spectral branches are independent of each other. From Table 2, it is not hard to see that baseline achieved the worst performance compared to other methods, which illustrates the importance of interaction between the two branches.


**Table 2.** The quantiative evaluation result of ablation study, the best value is in bold.

### 4.4.1. Effect of the PA

Inspired of [50], we add pixel attention (PA) block in IF to improve information fusion performance. To verify the significance of the PA in the IF. We add the PA in IF based on the baseline, which is called "Base+PA".

As can be seen in Table 2, compared with baseline, "Base+PA" has significantly improved in all evaluation indexes. The reason is that PA can improve the expression ability of convolutions [50]. In particular, PA can automatically calculate the importance of each neuron in the feature maps for reconstruction according to the input features, and then rescale these neurons with the importance.

### 4.4.2. Effect of the SSA

To take full advantage of the advantageous information of the spectral branch and spatial branch, we use both spatial attention and spectral attention mechanisms in the SSA module. It can be seen in Table 2 that "Base+PA+SSA" has achieved better fusion performance than "Base+PA". This is because SSA can make use of the cross-attention mechanism. That is to enhance the spectral characteristics of the spatial branch by spectral attention in the spectral branch, while enhancing spatial characteristics of the spectral branch by using spatial attention of the spatial branch. In this way, the two branches can make up the weakness of each other by using their respective advantages, which are conducive to the final information fusion.

### 4.4.3. Effect of the IIB

In this section, we assess the effectiveness of the IIB. Comparing "Base+PA+SSA" with "Base+PA+SSA+IIB" in Table 2, we can observe that "Base+PA+SSA", without any information interaction, has worse results in all objective indicators. This is because SSA

can take into account the information from another branch before generating attention. Through the information interaction, the network with SSA can obtain more appropriate and accurate attention weights. Moreover, the spectral-spatial information cannot be effectively incorporated by the IF module without information interactions. With the increase of information interaction, the fusion performance of the network is steadily improved as shown in Figure 6.

**Figure 6.** Compare network performance and parameters configured with different parameters. (**a**) the result of SAM. (**b**) the result of ERGAS. (**c**) the result of Q2n. (**d**) the result of CC.
