Modulation Learning on Fourier-Domain for Road Extraction From Remote Sensing Images | IEEE Journals & Magazine | IEEE Xplore

Modulation Learning on Fourier-Domain for Road Extraction From Remote Sensing Images


Abstract:

Extraction road from remote sensing (RS) images is a challenging topic because of the inhomogeneous intensity, nonconsistent contrast, and very cluttered background of sa...Show More

Abstract:

Extraction road from remote sensing (RS) images is a challenging topic because of the inhomogeneous intensity, nonconsistent contrast, and very cluttered background of satellite images. Most previous approaches, relying on convolutions or self-attention, are built on the local operation or global modeling on the spatial domain but are difficult to capture weak and continuous road objects. The spectral representation of road image features and modulation learning on it provides a novel long-range-dependent and fine-grained feature representation mechanism. Based on it, we propose a novel road extraction network on RS images, called an adaptive Fourier filtered U-shaped network (AFU-Net) in this letter, which relies on modulation learning on the Fourier domain. The AFU-Net is composed of modulation learner (MoL) basic blocks and follows the pipeline of the classical U-Net model. The basic MoL block includes a global MoL (GML) block for global spectral modulation learning and an attentive MoL (AML) block which contains two parallel layers, i.e., phase-modulated filter (PMF) and magnitude-modulated filter (MMF), for fine-grained spectral modulation on the Fourier spectrum. The experiments on two public datasets, such as Massachusetts roads and DeepGlobe road datasets have shown the outstanding performance of AFU-Net on the metrics of accuracy, precision, recall, and mean intersection over union (mIoU).
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)
Article Sequence Number: 5000705
Date of Publication: 18 January 2023

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