A Cascaded Network With Coupled High-Low Frequency Features for Building Extraction

Abstract

Accurately extracting buildings from highresolution remote sensing images is crucial for human productivity and livelihood in urban areas. Due to varying scales and indistinct boundaries of buildings, it is crucial to fully leverage the high-and low-frequency features in building extraction from remote sensing images. However, previous studies have solely relied on either lowor high-frequency features, leading to errors like omissions or internal holes in the detected buildings at various scales. Although some studies have considered the integration between both highand low-frequency features, they overlook the suitability of different network depths for extracting different frequency features. A novel network called Cascaded Inception Conv-Former Network (CICF-Net) is proposed in this study to solve these problems. It leverages the parallel combination of convolutional neural network and Transformer to efficiently extract high-and low-frequency features for building extraction. In the encoder, as the network depth grows, we gradually reduce the contribution of high-frequency branch and enhance the focus on low-frequency branch. Moreover, a cascaded fusion strategy is employed to extract and integrate multi-scale high-and lowfrequency features. Meanwhile, we propose gated convolution UperNet as the decoder, which utilizes recursive gated convolution to facilitate multi-level spatial interactions and better restoration of fine-grained spatial details for building segmentation. The proposed CICF-Net achieves competitive accuracies on three public benchmarks: Massachusetts Building Dataset, WHU Aerial Building Dataset, and Inria Aerial Image Labeling Dataset, with IoU of 75.17%, 91.45%, and 81.28%, respectively. This provides strong evidence of its effectiveness in building extraction, as it can accurately capture spatial details and context of buildings.