research-article

Multi-Stage Fusion and Multi-Source Attention Network for Multi-Modal Remote Sensing Image Segmentation

Authors:

Rui YaoAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 12, Issue 6

Article No.: 82, Pages 1 - 20

https://doi.org/10.1145/3484440

Published: 20 December 2021 Publication History

Abstract

With the rapid development of sensor technology, lots of remote sensing data have been collected. It effectively obtains good semantic segmentation performance by extracting feature maps based on multi-modal remote sensing images since extra modal data provides more information. How to make full use of multi-model remote sensing data for semantic segmentation is challenging. Toward this end, we propose a new network called Multi-Stage Fusion and Multi-Source Attention Network ((MS)²-Net) for multi-modal remote sensing data segmentation. The multi-stage fusion module fuses complementary information after calibrating the deviation information by filtering the noise from the multi-modal data. Besides, similar feature points are aggregated by the proposed multi-source attention for enhancing the discriminability of features with different modalities. The proposed model is evaluated on publicly available multi-modal remote sensing data sets, and results demonstrate the effectiveness of the proposed method.

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Index Terms

Multi-Stage Fusion and Multi-Source Attention Network for Multi-Modal Remote Sensing Image Segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
      2. Computer vision tasks
        Scene understanding

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 12, Issue 6

December 2021

356 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3501281

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 20 December 2021

Accepted: 01 August 2021

Revised: 01 July 2021

Received: 01 December 2020

Published in TIST Volume 12, Issue 6

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Xu RSamat AZhu ELi ELi W(2024)Unsupervised Domain Adaptation with Contrastive Learning-Based Discriminative Feature Augmentation for RS Image ClassificationRemote Sensing10.3390/rs1611197416:11(1974)Online publication date: 30-May-2024
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Wang NWu QGui YHu QLi W(2024)Cross-Modal Segmentation Network for Winter Wheat Mapping in Complex Terrain Using Remote-Sensing Multi-Temporal Images and DEM DataRemote Sensing10.3390/rs1610177516:10(1775)Online publication date: 16-May-2024
https://doi.org/10.3390/rs16101775
Han ZTian YZheng CZhao F(2024)Forest Fire Smoke Detection Based on Multiple Color Spaces Deep Feature FusionForests10.3390/f1504068915:4(689)Online publication date: 11-Apr-2024
https://doi.org/10.3390/f15040689
Sui BCao YCheng HZhang SZhu JXie Y(2024)Detail-Optimized Super-Resolution Reconstruction-Based Multistage Training Strategy for Remote Sensing Semantic SegmentationIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2023.333984262(1-16)Online publication date: 2024
https://doi.org/10.1109/TGRS.2023.3339842
Ye ZLi ZWang NLi YLi W(2024)HR and LiDAR Data Collaborative Semantic Segmentation Based on Adaptive Cross-Modal Fusion NetworkIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.341838717(12153-12168)Online publication date: 2024
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Zhang YGao DDu YLi BQin L(2024)Efficient multi-scale network for semantic segmentation of fine-resolution remotely sensed imagesMeasurement Science and Technology10.1088/1361-6501/ad50fa35:9(096005)Online publication date: 5-Jun-2024
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