## 1 Introduction
Reference [17] indicates that noise may be amplified as the feature maps propagate to deeper layers of the network. This phenomenon highlights the relatively weak noise robustness of Convolutional Neural Networks (CNNs). The reason behind this is that pooling operations and strided convolution can cause the aliasing of different frequency components, resulting in the contamination of low-frequency components with high-frequency components (noise). This aliasing disrupts the integrity of low-frequency components (the basic structure of the image), thereby reducing the performance of CNNs. In digital signal processing, utilizing frequency domain analysis to enhance specific frequencies while suppressing others can effectively provide filter selectivity. Assuming that suppressing high-frequency components in the backbone network can enhance its noise robustness, it is essential to note that convolutional neural networks excel at extracting spatial features. Essentially, convolutional neural networks, as methods for analyzing signals in the spatial domain, cannot effectively suppress high-frequency components. Therefore, integrating frequency domain analysis methods with convolutional neural networks is a highly effective approach. FeaNet [18] demonstrated that Global Average Pooling (GAP) is a special case of the 2D Discrete Cosine Transform (DCT), equivalent to the lowest frequency component of the DCT. From the perspective of frequency analysis, using only GAP is equivalent to disregarding information about other valuable frequency components in the features. Selectively incorporating high-frequency components helps to address the issue of inadequate information utilized in the network. The 2D Discrete Wavelet Transform (DWT) can separate the basic structure and details of an image. After the transformation, the image is divided into four components. The low-frequency component preserves the fundamental structure of the image, while the high-frequency components retain detailed information but also introduce noise. Combining DWT and CNNs is a good attempt; selectively removing specific high-frequency components can effectively enhance the network's noise robustness. Simultaneously, integrating partial high-frequency components effectively resolves the issue of inadequate information utilized in the network. In remote sensing images, many objects exhibit significant variations in size, aspect ratio, dense distribution, and occlusion. Successful detection of such objects relies on extra contextual information; therefore, the effective receptive field size of the network is highly sensitive. The above viewpoint has been verified by Reference [15], and we further elaborate on this perspective. 1. Enriched contextual information boosts the accuracy of the detector. As illustrated in Figure 1a, vehicles and boats typically exhibit similar shapes in remote sensing images due to their modest imaging sizes, leading to classification errors. The occurrence of these errors is because the detector only considers a small range of contextual information. Increasing the effective receptive field introduces more background information, facilitating effective and accurate classification based on whether the background is sea or road surface. 2. Correctly detecting different objects requires varying effective receptive field sizes. Different objects have different sizes, degrees of occlusion, and backdrop complexity, leading to varying necessary receptive field sizes for successful detection. For instance, in unobstructed scenes such as aircraft, swimming pools, and tennis courts, relatively smaller receptive fields are required. However, at intersections, bridges, docks, etc., extremely large receptive fields are required because these types of objects often have large sizes and aspect ratios and are obscured by other objects, making accurate recognition challenging. As illustrated in Figure 1b, the tennis court in the upper image is clear and unobstructed, requiring only a smaller receptive field for accurate recognition.