# DFENet: A Novel Dual-Path Feature Extraction Network for Semantic Segmentation of Remote Sensing Images

## TL;DR

This paper introduces DFENet, a new network for segmenting remote sensing images that improves accuracy by combining spatial and frequency-domain features.

## Contribution

DFENet introduces a dual-path module and a frequency-domain feature extraction block to enhance segmentation performance in remote sensing.

## Key findings

- DFENet achieves 83.09% mIoU on the ISPRS Vaihingen dataset.
- The method reaches 86.05% mIoU on the ISPRS Potsdam dataset.
- The dual-path module effectively captures global and local features.

## Abstract

Semantic segmentation of remote sensing images (RSIs) is a fundamental task in geoscience research. However, designing efficient feature fusion modules remains challenging for existing dual-branch or multi-branch architectures. Furthermore, existing deep learning-based architectures predominantly concentrate on spatial feature modeling and context capturing while inherently neglecting the exploration and utilization of critical frequency-domain features, which is crucial for addressing issues of semantic confusion and blurred boundaries in complex remote sensing scenes. To address the challenges of feature fusion and the lack of frequency-domain information, we propose a novel dual-path feature extraction network (DFENet) in this paper. Specifically, a dual-path module (DPM) is developed in DFENet to extract global and local features, respectively. In the global path, after applying the channel splitting strategy, four feature extraction strategies are innovatively integrated to extract global features from different granularities. According to the strategy of supplementing frequency-domain information, a frequency-domain feature extraction block (FFEB) dominated by discrete Wavelet transform (DWT) is designed to effectively captures both high- and low-frequency components. Experimental results show that our method outperforms existing state-of-the-art methods in terms of segmentation performance, achieving a mean intersection over union (mIoU) of 83.09% on the ISPRS Vaihingen dataset and 86.05% on the ISPRS Potsdam dataset.