# Feature Fusion through Multitask CNN for Large-scale Remote Sensing Image Segmentation

Source: [Feature Fusion through Multitask CNN for Large-scale Remote Sensing Image Segmentation](<https://www.sota2.com/research/paper/68fa649a1e209131cea05779>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:07:03.168Z

Authors: Shihao Sun, Lei Yang, Wenjie Liu, Ruirui Li

Published: 2018-07-24T12:48:15.000Z

## Abstract

In recent years, Fully Convolutional Networks (FCN) has been widely used in various semantic segmentation tasks, including multi-modal remote sensing imagery. How to fuse multi-modal data to improve the segmentation performance has always been a research hotspot. In this paper, a novel end-toend fully convolutional neural network is proposed for semantic segmentation of natural color, infrared imagery and Digital Surface Models (DSM). It is based on a modified DeepUNet and perform the segmentation in a multi-task way. The channels are clustered into groups and processed on different task pipelines. After a series of segmentation and fusion, their shared features and private features are successfully merged together. Experiment results show that the feature fusion network is efficient. And our approach achieves good performance in ISPRS Semantic Labeling Contest (2D).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.09072>)

[PDF](<https://arxiv.org/pdf/1807.09072.pdf>)