## 1 Introduction
By leveraging depthwise separable convolutions, MobileNetV2 dramatically reduces both the parameter count and computational complexity. Additionally, its inverted residual structure and linear bottleneck design enhance the model's ability to extract high-dimensional features. Next, the encoder structure is used to extract shallow and deep spatial features. During deep feature extraction, an Atrous Bottleneck Pyramid Module (ABPM) is employed to mitigate issues such as feature discontinuities and spatial gaps caused by the original ASPP. To incorporate frequency domain features, this study proposes a Frequency Band Feature Separator (FBFS), which uses Haar wavelet transform to decompose frequency domain features into high-frequency and low-frequency signals. These are then converted into high- and low-frequency features that can be embedded into the CNN. Subsequently, a Domain Fusion Module (DFM) aligns and integrates spatial and frequency domain features, enabling effective cross-domain feature fusion. By utilizing high- and low-frequency features from the frequency domain to complement shallow and deep spatial features, the model can better capture areas with significant grayscale changes, such as shadows cast by buildings and trees, road edges, and boundaries between different surface materials. This enhances the accuracy and robustness of road extraction. In summary, the contributions in this study are as follows:  
1. Lightweight Backbone Network: The advantages of using the modified Xception as the backbone network are more apparent in scenarios where the segmented images have diverse semantic labels and the dataset size is sufficient for training. In contrast, the MobileNetV2 network has a simpler structure, fewer parameters, lower computational complexity, and faster training speed. For tasks like road extraction with fewer semantic label categories, MobileNetV2 achieves better segmentation accuracy. 2. Atrous Bottleneck Pyramid Module (ABPM): The original ASPP module extracts feature maps with different receptive fields by parallel atrous convolutions with various dilation rates. However, its spaced and sparse sampling leads to a lack of correlation between convolution results, causing issues like checkerboard artifacts, feature discontinuities, and spatial gaps. A common solution is to apply upsampling before convolution to avoid these artifacts. Inspired by MobileNetV2's inverted residual structure, which first expands dimensions, then applies convolution, and finally reduces dimensions while maintaining low computational costs through depthwise separable convolution, this study designs the ABPM. This module extracts features using atrous convolutions in high-dimensional spaces, improving the correlation between extracted features. 3. Introducing Frequency Domain Information: A Frequency Band Feature Separator (FBFS) is designed to split the frequency domain features into high-frequency and low-frequency components using Haar wavelet transform. High-frequency features focus on local edges, while low-frequency features emphasize large-area continuous regions and regions. Subsequently, a Domain Fusion Module (DFM) aligns and fuses frequency domain and spatial domain features. High-frequency features are integrated with deep spatial features that carry semantic information, whereas low-frequency features are linked with shallow spatial features that encode positional information. DFM can select features between the frequency domain and spatial domain, effectively bridge the semantic gap between the two features, and make full use of the frequency domain and spatial domain feature information.