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Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images

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conference contribution
posted on 2022-07-04, 10:21 authored by Y Lin, H Sun, N Liu, Y Bian, J Cen, Huiyu Zhou

Home  Artificial Neural Networks and Machine Learning – ICANN 2022  Conference paper

Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images

Yuhan Lin, Han Sun, Ningzhong Liu, Yetong Bian, Jun Cen & Huiyu Zhou 

Conference paper

First Online: 07 September 2022

2054 Accesses


7 Citations


Part of the Lecture Notes in Computer Science book series (LNCS,volume 13529)


Abstract

Due to the extreme complexity of scale and shape as well as the uncertainty of the predicted location, salient object detection in optical remote sensing images (RSI-SOD) is a very difficult task. The existing SOD methods can satisfy the detection performance for natural scene images, but they are not well adapted to RSI-SOD due to the above-mentioned image characteristics in remote sensing images. In this paper, we propose a novel Attention Guided Network (AGNet) for SOD in optical RSIs, including position enhancement stage and detail refinement stage. Specifically, the position enhancement stage consists of a semantic attention module and a contextual attention module to accurately describe the approximate location of salient objects. The detail refinement stage uses the proposed self-refinement module to progressively refine the predicted results under the guidance of attention and reverse attention. In addition, the hybrid loss is applied to supervise the training of the network, which can improve the performance of the model from three perspectives of pixel, region and statistics. Extensive experiments on two popular benchmarks demonstrate that AGNet achieves competitive performance compared to other state-of-the-art methods. The code will be available at https://github.com/NuaaYH/AGNet.

History

Source

ICANN 2022 - 31st International Conference on Artificial Neural Networks, University of the West of England, in Bristol, United Kingdom, 6 to 9 September 2022

Version

  • AM (Accepted Manuscript)

Published in

Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022.

Publisher

Springer

isbn

978-3-031-15919-0

Acceptance date

2022-07-02

Copyright date

2022

Available date

2023-11-21

Book series

Lecture Notes in Computer Science vol 13529.

Temporal coverage: start date

2022-09-06

Temporal coverage: end date

2022-09-09

Language

en

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