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

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.

Keywords

This work is supported in part by the Fundamental Research Funds for the Central Universities of China under Grant NZ2019009.

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Correspondence to Han Sun .

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Lin, Y., Sun, H., Liu, N., Bian, Y., Cen, J., Zhou, H. (2022). Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. Springer, Cham. https://doi.org/10.1007/978-3-031-15919-0_3

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  • DOI: https://doi.org/10.1007/978-3-031-15919-0_3

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  • Online ISBN: 978-3-031-15919-0

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