## 1 Introduction
However, recognizing the tennis court in the lower image requires a broader receptive field, as the tennis court is largely obscured by tree shadows, making recognition based on appearance alone challenging. To address these issues: (a) The noise in Remote Sensing images is amplified as the feature maps are passed to deeper layers of the network, thereby affecting the detector's performance. (b) Successful detection of remote sensing objects necessitates appropriate sizes of contextual information. In this article, we propose a feature extraction backbone network suitable for remote sensing image object detection, termed the Wavelet-Domain Adaptive Receptive Field Network (WDARFNet). WDARFNet can extract frequency domain features, permitting the network to selectively eliminate certain high-frequency components to efficaciously enhance network noise robustness, with the objective of suppressing noise and precluding the loss of tiny object information during the down-sampling process. This provides more usable information for classification and regression and obtaining more accurate detection results. Furthermore, we propose a paradigm that integrates the selective kernel mechanism with the frequency domain analysis method, enabling the network to dynamically adjust the receptive field based on the object. The main contributions of this article are as follows. To address the issue of insufficient information utilization in the network and mitigate the impact of noise on its performance, we integrate Convolutional Neural Networks (CNNs) with frequency domain analysis techniques. This integration results in a unified model, which leads to the proposal of the Wavelet Domain Channel Selection Module (WDCSM) and the Wavelet Domain Spatial Selection Module (WDSSM). Both WDCSM and WDSSM effectively combine low-frequency and high-frequency components, thereby capturing additional contours and detailed information, which enhance the network's representational capacity. Moreover, by eliminating diagonal high-frequency components that contain substantial noise, the network's noise robustness is improved. The WDCSM explicitly models the interdependencies between channels of feature maps and leverages global information to adaptively recalibrate the channel-wise feature responses, thereby further enhancing the network's representational capacity. The WDSSM is capable of selecting the most suitable spatial feature information for detecting objects from various scales of large-kernel branches, thereby dynamically adjusting the receptive field size for each object in accordance with their spatial needs. 4. To adaptively adjust the receptive field of the backbone network, we integrate the selective kernel mechanism with the WDSSM. The selective kernel mechanism is implemented through a multi-branch large-kernel network, and the WDSSM effectively assigns weights to the features processed by large kernels in each branch and spatially integrates the features, thereby permitting the network to dynamically adjust the receptive field for each object in space according to specific requirements. 5. Extensive experiments have been conducted to validate the effectiveness of our proposed model on three widely used datasets: DOTA-v1.0, DIOR-R, and HRSC2016. Figure 1: Successfully detecting remote sensing objects requires appropriate contextual information. (**a**) cars vs. ships—small objects need local background; (**b**) occluded objects need long-range contextual information.