Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images
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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 2022Version
- AM (Accepted Manuscript)